Importing relevant libraries

library(data.table)
library(mltools)
library(DMwR)
library(plyr)
library(dplyr)
library(caTools)
library(caret)
library(e1071)
library(corrplot)
library("arules")
library(nnet)
library(randomForest)
library(Boruta)
#install.packages("scales")
library(plotly)
#install.packages('arulesViz')
set.seed(1000)

Importing dataset for initial preparation

clamp <- fread("ClaMP_Raw-5184.csv")
clamp <- lapply(clamp,  as.numeric)
clamp <- data.frame(clamp)
str(clamp)
'data.frame':   5184 obs. of  56 variables:
 $ e_magic                    : num  23117 23117 23117 23117 23117 ...
 $ e_cblp                     : num  144 144 144 144 144 80 144 144 144 144 ...
 $ e_cp                       : num  3 3 3 3 3 2 3 3 3 3 ...
 $ e_crlc                     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_cparhdr                  : num  4 4 4 4 4 4 4 4 4 4 ...
 $ e_minalloc                 : num  0 0 0 0 0 15 0 0 0 0 ...
 $ e_maxalloc                 : num  65535 65535 65535 65535 65535 ...
 $ e_ss                       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_sp                       : num  184 184 184 184 184 184 184 184 184 184 ...
 $ e_csum                     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_ip                       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_cs                       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_lfarlc                   : num  64 64 64 64 64 64 64 64 64 64 ...
 $ e_ovno                     : num  0 0 0 0 0 26 0 0 0 0 ...
 $ e_res                      : num  NA NA NA NA NA NA NA NA NA NA ...
 $ e_oemid                    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_oeminfo                  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_res2                     : num  NA NA NA NA NA NA NA NA NA NA ...
 $ e_lfanew                   : num  256 184 272 184 224 256 272 256 240 224 ...
 $ Machine                    : num  332 332 332 332 332 332 332 332 332 332 ...
 $ NumberOfSections           : num  4 4 5 1 5 8 8 5 5 6 ...
 $ CreationYear               : num  2006 1999 2012 2011 2012 ...
 $ PointerToSymbolTable       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ NumberOfSymbols            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SizeOfOptionalHeader       : num  224 224 224 224 224 224 224 224 224 224 ...
 $ Characteristics            : num  8450 8462 8450 8450 258 ...
 $ Magic                      : num  267 267 267 267 267 267 267 267 267 267 ...
 $ MajorLinkerVersion         : num  8 5 9 9 10 2 6 8 10 9 ...
 $ MinorLinkerVersion         : num  0 10 0 0 10 25 0 0 10 0 ...
 $ SizeOfCode                 : num  1100288 4096 27648 0 11776 ...
 $ SizeOfInitializedData      : num  225792 2560 20480 87552 36352 ...
 $ SizeOfUninitializedData    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AddressOfEntryPoint        : num  1069880 7680 28859 0 13379 ...
 $ BaseOfCode                 : num  4096 4096 4096 4096 4096 ...
 $ BaseOfData                 : num  1110016 8192 32768 4096 16384 ...
 $ ImageBase                  : num  1.18e+09 2.68e+08 2.68e+08 2.68e+08 4.19e+06 ...
 $ SectionAlignment           : num  4096 4096 4096 4096 4096 ...
 $ FileAlignment              : num  512 512 512 512 512 ...
 $ MajorOperatingSystemVersion: num  4 4 5 6 6 1 4 4 6 5 ...
 $ MinorOperatingSystemVersion: num  0 0 0 1 2 0 0 0 2 0 ...
 $ MajorImageVersion          : num  0 0 0 6 6 0 0 0 6 0 ...
 $ MinorImageVersion          : num  0 0 0 1 2 0 0 0 2 0 ...
 $ MajorSubsystemVersion      : num  5 4 5 5 6 4 4 4 6 5 ...
 $ MinorSubsystemVersion      : num  1 0 0 0 2 0 0 0 2 0 ...
 $ SizeOfImage                : num  1335296 20480 61440 94208 57344 ...
 $ SizeOfHeaders              : num  1024 1024 1024 512 1024 ...
 $ CheckSum                   : num  1194954 0 67688 113668 69089 ...
 $ Subsystem                  : num  3 2 2 2 2 2 2 2 3 3 ...
 $ DllCharacteristics         : num  64 0 320 1344 33088 ...
 $ SizeOfStackReserve         : num  1048576 1048576 1048576 1048576 262144 ...
 $ SizeOfStackCommit          : num  4096 4096 4096 4096 8192 ...
 $ SizeOfHeapReserve          : num  1048576 1048576 1048576 1048576 1048576 ...
 $ SizeOfHeapCommit           : num  4096 4096 4096 4096 4096 ...
 $ LoaderFlags                : num  0 0 0 0 0 0 0 0 0 0 ...
 $ NumberOfRvaAndSizes        : num  16 16 16 16 16 16 16 16 16 16 ...
 $ class                      : num  0 0 0 0 0 0 0 0 0 0 ...

Remove empty columns

clamp$e_res <- NULL
clamp$e_res2 <- NULL
clamp$e_magic <- NULL
clamp$e_crlc <- NULL

row.has.na <- apply(clamp, 1, function(x){any(is.na(x))})
row.with.na <- clamp[row.has.na,]

str(clamp)
'data.frame':   5184 obs. of  52 variables:
 $ e_cblp                     : num  144 144 144 144 144 80 144 144 144 144 ...
 $ e_cp                       : num  3 3 3 3 3 2 3 3 3 3 ...
 $ e_cparhdr                  : num  4 4 4 4 4 4 4 4 4 4 ...
 $ e_minalloc                 : num  0 0 0 0 0 15 0 0 0 0 ...
 $ e_maxalloc                 : num  65535 65535 65535 65535 65535 ...
 $ e_ss                       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_sp                       : num  184 184 184 184 184 184 184 184 184 184 ...
 $ e_csum                     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_ip                       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_cs                       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_lfarlc                   : num  64 64 64 64 64 64 64 64 64 64 ...
 $ e_ovno                     : num  0 0 0 0 0 26 0 0 0 0 ...
 $ e_oemid                    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_oeminfo                  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ e_lfanew                   : num  256 184 272 184 224 256 272 256 240 224 ...
 $ Machine                    : num  332 332 332 332 332 332 332 332 332 332 ...
 $ NumberOfSections           : num  4 4 5 1 5 8 8 5 5 6 ...
 $ CreationYear               : num  2006 1999 2012 2011 2012 ...
 $ PointerToSymbolTable       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ NumberOfSymbols            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SizeOfOptionalHeader       : num  224 224 224 224 224 224 224 224 224 224 ...
 $ Characteristics            : num  8450 8462 8450 8450 258 ...
 $ Magic                      : num  267 267 267 267 267 267 267 267 267 267 ...
 $ MajorLinkerVersion         : num  8 5 9 9 10 2 6 8 10 9 ...
 $ MinorLinkerVersion         : num  0 10 0 0 10 25 0 0 10 0 ...
 $ SizeOfCode                 : num  1100288 4096 27648 0 11776 ...
 $ SizeOfInitializedData      : num  225792 2560 20480 87552 36352 ...
 $ SizeOfUninitializedData    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AddressOfEntryPoint        : num  1069880 7680 28859 0 13379 ...
 $ BaseOfCode                 : num  4096 4096 4096 4096 4096 ...
 $ BaseOfData                 : num  1110016 8192 32768 4096 16384 ...
 $ ImageBase                  : num  1.18e+09 2.68e+08 2.68e+08 2.68e+08 4.19e+06 ...
 $ SectionAlignment           : num  4096 4096 4096 4096 4096 ...
 $ FileAlignment              : num  512 512 512 512 512 ...
 $ MajorOperatingSystemVersion: num  4 4 5 6 6 1 4 4 6 5 ...
 $ MinorOperatingSystemVersion: num  0 0 0 1 2 0 0 0 2 0 ...
 $ MajorImageVersion          : num  0 0 0 6 6 0 0 0 6 0 ...
 $ MinorImageVersion          : num  0 0 0 1 2 0 0 0 2 0 ...
 $ MajorSubsystemVersion      : num  5 4 5 5 6 4 4 4 6 5 ...
 $ MinorSubsystemVersion      : num  1 0 0 0 2 0 0 0 2 0 ...
 $ SizeOfImage                : num  1335296 20480 61440 94208 57344 ...
 $ SizeOfHeaders              : num  1024 1024 1024 512 1024 ...
 $ CheckSum                   : num  1194954 0 67688 113668 69089 ...
 $ Subsystem                  : num  3 2 2 2 2 2 2 2 3 3 ...
 $ DllCharacteristics         : num  64 0 320 1344 33088 ...
 $ SizeOfStackReserve         : num  1048576 1048576 1048576 1048576 262144 ...
 $ SizeOfStackCommit          : num  4096 4096 4096 4096 8192 ...
 $ SizeOfHeapReserve          : num  1048576 1048576 1048576 1048576 1048576 ...
 $ SizeOfHeapCommit           : num  4096 4096 4096 4096 4096 ...
 $ LoaderFlags                : num  0 0 0 0 0 0 0 0 0 0 ...
 $ NumberOfRvaAndSizes        : num  16 16 16 16 16 16 16 16 16 16 ...
 $ class                      : num  0 0 0 0 0 0 0 0 0 0 ...

Based on the structure observed, we add and rename columns to make dataset more relevant for Tesla

# Rename columns
colnames(clamp)[which(names(clamp) == "ImageBase")] <- "ChargeCycles"
colnames(clamp)[which(names(clamp) == "SizeOfImage")] <- "CarMileage"
colnames(clamp)[which(names(clamp) == "CreationYear")] <- "YearObtained"
colnames(clamp)[which(names(clamp) == "MajorSubsystemVersion")] <- "SoftwareVersion"
colnames(clamp)[which(names(clamp) == "MinorSubsystemVersion")] <- "OSVersion"
colnames(clamp)[which(names(clamp) == "Machine")] <- "Models"
colnames(clamp)[which(names(clamp) == "class")] <- "MalwareDetection"

# Adding in new column
teslacountries <- fread("TeslaCountries.csv")
clamp <- clamp %>% left_join(teslacountries, by = c("e_lfanew" = "CountryID"))
clamp$e_lfanew <- NULL
#clamp$Country <- as.factor(clamp$Country)

Converting to numeric columns

clamp_num_names <- c("NumberOfSections", "NumberOfSymbols", "SizeOfOptionalHeader", "ChargeCycles",  "SizeOfInitializedData", "SizeOfUninitializedData", "AddressOfEntryPoint", "BaseOfCode", "BaseOfData", "SizeOfCode", "CarMileage", "SizeOfHeaders", "CheckSum", "SizeOfStackReserve", "SizeOfStackCommit", "SizeOfHeapReserve", "SizeOfHeapCommit", "NumberOfRvaAndSizes")
clamp_num <- clamp[names(clamp) %in% clamp_num_names]
num_names <- names(clamp_num)
clamp_num <- lapply(clamp_num, as.numeric)
clamp_num <- data.frame(clamp_num)
str(clamp_num)
'data.frame':   5184 obs. of  18 variables:
 $ NumberOfSections       : num  4 4 5 1 5 8 8 5 5 6 ...
 $ NumberOfSymbols        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SizeOfOptionalHeader   : num  224 224 224 224 224 224 224 224 224 224 ...
 $ SizeOfCode             : num  1100288 4096 27648 0 11776 ...
 $ SizeOfInitializedData  : num  225792 2560 20480 87552 36352 ...
 $ SizeOfUninitializedData: num  0 0 0 0 0 0 0 0 0 0 ...
 $ AddressOfEntryPoint    : num  1069880 7680 28859 0 13379 ...
 $ BaseOfCode             : num  4096 4096 4096 4096 4096 ...
 $ BaseOfData             : num  1110016 8192 32768 4096 16384 ...
 $ ChargeCycles           : num  1.18e+09 2.68e+08 2.68e+08 2.68e+08 4.19e+06 ...
 $ CarMileage             : num  1335296 20480 61440 94208 57344 ...
 $ SizeOfHeaders          : num  1024 1024 1024 512 1024 ...
 $ CheckSum               : num  1194954 0 67688 113668 69089 ...
 $ SizeOfStackReserve     : num  1048576 1048576 1048576 1048576 262144 ...
 $ SizeOfStackCommit      : num  4096 4096 4096 4096 8192 ...
 $ SizeOfHeapReserve      : num  1048576 1048576 1048576 1048576 1048576 ...
 $ SizeOfHeapCommit       : num  4096 4096 4096 4096 4096 ...
 $ NumberOfRvaAndSizes    : num  16 16 16 16 16 16 16 16 16 16 ...

Converting the remaining to categorical columns

clamp_cat <- clamp
clamp_cat[, num_names] <- list(NULL)
clamp_cat <- lapply(clamp_cat, factor)
clamp_cat <- data.frame(clamp_cat)
str(clamp_cat)
'data.frame':   5184 obs. of  34 variables:
 $ e_cblp                     : Factor w/ 9 levels "0","10","46",..: 8 8 8 8 8 5 8 8 8 8 ...
 $ e_cp                       : Factor w/ 7 levels "0","1","2","3",..: 4 4 4 4 4 3 4 4 4 4 ...
 $ e_cparhdr                  : Factor w/ 3 levels "0","2","4": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_minalloc                 : Factor w/ 4 levels "0","15","16",..: 1 1 1 1 1 2 1 1 1 1 ...
 $ e_maxalloc                 : Factor w/ 3 levels "0","17744","65535": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_ss                       : Factor w/ 2 levels "0","65520": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_sp                       : Factor w/ 8 levels "0","40","64",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ e_csum                     : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_ip                       : Factor w/ 4 levels "0","256","1047",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_cs                       : Factor w/ 4 levels "0","18293","18919",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_lfarlc                   : Factor w/ 3 levels "0","64","65": 2 2 2 2 2 2 2 2 2 2 ...
 $ e_ovno                     : Factor w/ 2 levels "0","26": 1 1 1 1 1 2 1 1 1 1 ...
 $ e_oemid                    : Factor w/ 2 levels "0","267": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_oeminfo                  : Factor w/ 3 levels "0","6","8": 1 1 1 1 1 1 1 1 1 1 ...
 $ Models                     : Factor w/ 3 levels "332","448","34404": 1 1 1 1 1 1 1 1 1 1 ...
 $ YearObtained               : Factor w/ 36 levels "1970","1971",..: 16 9 22 21 22 4 22 22 22 20 ...
 $ PointerToSymbolTable       : Factor w/ 9 levels "0","36384","109088",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Characteristics            : Factor w/ 42 levels "34","35","258",..: 27 30 27 27 3 40 30 27 3 11 ...
 $ Magic                      : Factor w/ 2 levels "0","267": 2 2 2 2 2 2 2 2 2 2 ...
 $ MajorLinkerVersion         : Factor w/ 23 levels "0","1","2","3",..: 9 6 10 10 11 3 7 9 11 10 ...
 $ MinorLinkerVersion         : Factor w/ 36 levels "0","1","2","3",..: 1 11 1 1 11 22 1 1 11 1 ...
 $ SectionAlignment           : Factor w/ 6 levels "0","128","256",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ FileAlignment              : Factor w/ 7 levels "0","128","256",..: 4 4 4 4 4 4 7 7 4 4 ...
 $ MajorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","4",..: 4 4 5 6 6 2 4 4 6 5 ...
 $ MinorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ MajorImageVersion          : Factor w/ 41 levels "0","1","2","3",..: 1 1 1 7 7 1 1 1 7 1 ...
 $ MinorImageVersion          : Factor w/ 53 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ SoftwareVersion            : Factor w/ 6 levels "0","1","3","4",..: 5 4 5 5 6 4 4 4 6 5 ...
 $ OSVersion                  : Factor w/ 5 levels "0","1","2","10",..: 2 1 1 1 3 1 1 1 3 1 ...
 $ Subsystem                  : Factor w/ 6 levels "0","1","2","3",..: 4 3 3 3 3 3 3 3 4 4 ...
 $ DllCharacteristics         : Factor w/ 25 levels "0","1","2","3",..: 5 1 7 12 17 1 1 1 17 17 ...
 $ LoaderFlags                : Factor w/ 6 levels "0","4357151",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ MalwareDetection           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ Country                    : Factor w/ 39 levels "Australia","Austria",..: 5 3 32 3 25 5 32 5 39 25 ...

Rename factor values (How to hide the revalue results?)

clamp_cat$Models <- revalue(clamp_cat$Models, c("332"="Model X", "448"="Model Y", '34404'= 'Model S'))
clamp_cat$OSVersion <- revalue(clamp_cat$OSVersion, c("0"="V5", "1"="V4", '2'= 'V3', '10' = 'V2', '20' = 'V1'))

Final dataset

clamp_model <- data.frame(clamp_cat, clamp_num)
str(clamp_model)
'data.frame':   5184 obs. of  52 variables:
 $ e_cblp                     : Factor w/ 9 levels "0","10","46",..: 8 8 8 8 8 5 8 8 8 8 ...
 $ e_cp                       : Factor w/ 7 levels "0","1","2","3",..: 4 4 4 4 4 3 4 4 4 4 ...
 $ e_cparhdr                  : Factor w/ 3 levels "0","2","4": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_minalloc                 : Factor w/ 4 levels "0","15","16",..: 1 1 1 1 1 2 1 1 1 1 ...
 $ e_maxalloc                 : Factor w/ 3 levels "0","17744","65535": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_ss                       : Factor w/ 2 levels "0","65520": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_sp                       : Factor w/ 8 levels "0","40","64",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ e_csum                     : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_ip                       : Factor w/ 4 levels "0","256","1047",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_cs                       : Factor w/ 4 levels "0","18293","18919",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_lfarlc                   : Factor w/ 3 levels "0","64","65": 2 2 2 2 2 2 2 2 2 2 ...
 $ e_ovno                     : Factor w/ 2 levels "0","26": 1 1 1 1 1 2 1 1 1 1 ...
 $ e_oemid                    : Factor w/ 2 levels "0","267": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_oeminfo                  : Factor w/ 3 levels "0","6","8": 1 1 1 1 1 1 1 1 1 1 ...
 $ Models                     : Factor w/ 3 levels "Model X","Model Y",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ YearObtained               : Factor w/ 36 levels "1970","1971",..: 16 9 22 21 22 4 22 22 22 20 ...
 $ PointerToSymbolTable       : Factor w/ 9 levels "0","36384","109088",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Characteristics            : Factor w/ 42 levels "34","35","258",..: 27 30 27 27 3 40 30 27 3 11 ...
 $ Magic                      : Factor w/ 2 levels "0","267": 2 2 2 2 2 2 2 2 2 2 ...
 $ MajorLinkerVersion         : Factor w/ 23 levels "0","1","2","3",..: 9 6 10 10 11 3 7 9 11 10 ...
 $ MinorLinkerVersion         : Factor w/ 36 levels "0","1","2","3",..: 1 11 1 1 11 22 1 1 11 1 ...
 $ SectionAlignment           : Factor w/ 6 levels "0","128","256",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ FileAlignment              : Factor w/ 7 levels "0","128","256",..: 4 4 4 4 4 4 7 7 4 4 ...
 $ MajorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","4",..: 4 4 5 6 6 2 4 4 6 5 ...
 $ MinorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ MajorImageVersion          : Factor w/ 41 levels "0","1","2","3",..: 1 1 1 7 7 1 1 1 7 1 ...
 $ MinorImageVersion          : Factor w/ 53 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ SoftwareVersion            : Factor w/ 6 levels "0","1","3","4",..: 5 4 5 5 6 4 4 4 6 5 ...
 $ OSVersion                  : Factor w/ 5 levels "V5","V4","V3",..: 2 1 1 1 3 1 1 1 3 1 ...
 $ Subsystem                  : Factor w/ 6 levels "0","1","2","3",..: 4 3 3 3 3 3 3 3 4 4 ...
 $ DllCharacteristics         : Factor w/ 25 levels "0","1","2","3",..: 5 1 7 12 17 1 1 1 17 17 ...
 $ LoaderFlags                : Factor w/ 6 levels "0","4357151",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ MalwareDetection           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ Country                    : Factor w/ 39 levels "Australia","Austria",..: 5 3 32 3 25 5 32 5 39 25 ...
 $ NumberOfSections           : num  4 4 5 1 5 8 8 5 5 6 ...
 $ NumberOfSymbols            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SizeOfOptionalHeader       : num  224 224 224 224 224 224 224 224 224 224 ...
 $ SizeOfCode                 : num  1100288 4096 27648 0 11776 ...
 $ SizeOfInitializedData      : num  225792 2560 20480 87552 36352 ...
 $ SizeOfUninitializedData    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AddressOfEntryPoint        : num  1069880 7680 28859 0 13379 ...
 $ BaseOfCode                 : num  4096 4096 4096 4096 4096 ...
 $ BaseOfData                 : num  1110016 8192 32768 4096 16384 ...
 $ ChargeCycles               : num  1.18e+09 2.68e+08 2.68e+08 2.68e+08 4.19e+06 ...
 $ CarMileage                 : num  1335296 20480 61440 94208 57344 ...
 $ SizeOfHeaders              : num  1024 1024 1024 512 1024 ...
 $ CheckSum                   : num  1194954 0 67688 113668 69089 ...
 $ SizeOfStackReserve         : num  1048576 1048576 1048576 1048576 262144 ...
 $ SizeOfStackCommit          : num  4096 4096 4096 4096 8192 ...
 $ SizeOfHeapReserve          : num  1048576 1048576 1048576 1048576 1048576 ...
 $ SizeOfHeapCommit           : num  4096 4096 4096 4096 4096 ...
 $ NumberOfRvaAndSizes        : num  16 16 16 16 16 16 16 16 16 16 ...
fwrite(clamp_model, file="TableauClampData.csv")
names(clamp_model)
 [1] "e_cblp"                      "e_cp"                        "e_cparhdr"                  
 [4] "e_minalloc"                  "e_maxalloc"                  "e_ss"                       
 [7] "e_sp"                        "e_csum"                      "e_ip"                       
[10] "e_cs"                        "e_lfarlc"                    "e_ovno"                     
[13] "e_oemid"                     "e_oeminfo"                   "Models"                     
[16] "YearObtained"                "PointerToSymbolTable"        "Characteristics"            
[19] "Magic"                       "MajorLinkerVersion"          "MinorLinkerVersion"         
[22] "SectionAlignment"            "FileAlignment"               "MajorOperatingSystemVersion"
[25] "MinorOperatingSystemVersion" "MajorImageVersion"           "MinorImageVersion"          
[28] "SoftwareVersion"             "OSVersion"                   "Subsystem"                  
[31] "DllCharacteristics"          "LoaderFlags"                 "MalwareDetection"           
[34] "Country"                     "NumberOfSections"            "NumberOfSymbols"            
[37] "SizeOfOptionalHeader"        "SizeOfCode"                  "SizeOfInitializedData"      
[40] "SizeOfUninitializedData"     "AddressOfEntryPoint"         "BaseOfCode"                 
[43] "BaseOfData"                  "ChargeCycles"                "CarMileage"                 
[46] "SizeOfHeaders"               "CheckSum"                    "SizeOfStackReserve"         
[49] "SizeOfStackCommit"           "SizeOfHeapReserve"           "SizeOfHeapCommit"           
[52] "NumberOfRvaAndSizes"        
clamp_corr <- clamp_model
clamp_corr <- data.frame(lapply(clamp_corr, as.numeric))
corrplot(cor(clamp_corr), type = "upper", title = "Correlation Plot for Final Dataset", mar=c(0,0,1,0),
         tl.cex=0.5,
         tl.col = "black")

str(clamp_model)
'data.frame':   5184 obs. of  52 variables:
 $ e_cblp                     : Factor w/ 9 levels "0","10","46",..: 8 8 8 8 8 5 8 8 8 8 ...
 $ e_cp                       : Factor w/ 7 levels "0","1","2","3",..: 4 4 4 4 4 3 4 4 4 4 ...
 $ e_cparhdr                  : Factor w/ 3 levels "0","2","4": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_minalloc                 : Factor w/ 4 levels "0","15","16",..: 1 1 1 1 1 2 1 1 1 1 ...
 $ e_maxalloc                 : Factor w/ 3 levels "0","17744","65535": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_ss                       : Factor w/ 2 levels "0","65520": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_sp                       : Factor w/ 8 levels "0","40","64",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ e_csum                     : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_ip                       : Factor w/ 4 levels "0","256","1047",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_cs                       : Factor w/ 4 levels "0","18293","18919",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_lfarlc                   : Factor w/ 3 levels "0","64","65": 2 2 2 2 2 2 2 2 2 2 ...
 $ e_ovno                     : Factor w/ 2 levels "0","26": 1 1 1 1 1 2 1 1 1 1 ...
 $ e_oemid                    : Factor w/ 2 levels "0","267": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_oeminfo                  : Factor w/ 3 levels "0","6","8": 1 1 1 1 1 1 1 1 1 1 ...
 $ Models                     : Factor w/ 3 levels "Model X","Model Y",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ YearObtained               : Factor w/ 36 levels "1970","1971",..: 16 9 22 21 22 4 22 22 22 20 ...
 $ PointerToSymbolTable       : Factor w/ 9 levels "0","36384","109088",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Characteristics            : Factor w/ 42 levels "34","35","258",..: 27 30 27 27 3 40 30 27 3 11 ...
 $ Magic                      : Factor w/ 2 levels "0","267": 2 2 2 2 2 2 2 2 2 2 ...
 $ MajorLinkerVersion         : Factor w/ 23 levels "0","1","2","3",..: 9 6 10 10 11 3 7 9 11 10 ...
 $ MinorLinkerVersion         : Factor w/ 36 levels "0","1","2","3",..: 1 11 1 1 11 22 1 1 11 1 ...
 $ SectionAlignment           : Factor w/ 6 levels "0","128","256",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ FileAlignment              : Factor w/ 7 levels "0","128","256",..: 4 4 4 4 4 4 7 7 4 4 ...
 $ MajorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","4",..: 4 4 5 6 6 2 4 4 6 5 ...
 $ MinorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ MajorImageVersion          : Factor w/ 41 levels "0","1","2","3",..: 1 1 1 7 7 1 1 1 7 1 ...
 $ MinorImageVersion          : Factor w/ 53 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ SoftwareVersion            : Factor w/ 6 levels "0","1","3","4",..: 5 4 5 5 6 4 4 4 6 5 ...
 $ OSVersion                  : Factor w/ 5 levels "V5","V4","V3",..: 2 1 1 1 3 1 1 1 3 1 ...
 $ Subsystem                  : Factor w/ 6 levels "0","1","2","3",..: 4 3 3 3 3 3 3 3 4 4 ...
 $ DllCharacteristics         : Factor w/ 25 levels "0","1","2","3",..: 5 1 7 12 17 1 1 1 17 17 ...
 $ LoaderFlags                : Factor w/ 6 levels "0","4357151",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ MalwareDetection           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ Country                    : Factor w/ 39 levels "Australia","Austria",..: 5 3 32 3 25 5 32 5 39 25 ...
 $ NumberOfSections           : num  4 4 5 1 5 8 8 5 5 6 ...
 $ NumberOfSymbols            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SizeOfOptionalHeader       : num  224 224 224 224 224 224 224 224 224 224 ...
 $ SizeOfCode                 : num  1100288 4096 27648 0 11776 ...
 $ SizeOfInitializedData      : num  225792 2560 20480 87552 36352 ...
 $ SizeOfUninitializedData    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AddressOfEntryPoint        : num  1069880 7680 28859 0 13379 ...
 $ BaseOfCode                 : num  4096 4096 4096 4096 4096 ...
 $ BaseOfData                 : num  1110016 8192 32768 4096 16384 ...
 $ ChargeCycles               : num  1.18e+09 2.68e+08 2.68e+08 2.68e+08 4.19e+06 ...
 $ CarMileage                 : num  1335296 20480 61440 94208 57344 ...
 $ SizeOfHeaders              : num  1024 1024 1024 512 1024 ...
 $ CheckSum                   : num  1194954 0 67688 113668 69089 ...
 $ SizeOfStackReserve         : num  1048576 1048576 1048576 1048576 262144 ...
 $ SizeOfStackCommit          : num  4096 4096 4096 4096 8192 ...
 $ SizeOfHeapReserve          : num  1048576 1048576 1048576 1048576 1048576 ...
 $ SizeOfHeapCommit           : num  4096 4096 4096 4096 4096 ...
 $ NumberOfRvaAndSizes        : num  16 16 16 16 16 16 16 16 16 16 ...

Data visualisation Numerical variables: Univariate histogram analysis

clamp_num1 <- clamp_model[, c("NumberOfSections", "NumberOfRvaAndSizes", "SizeOfOptionalHeader")]
ggplot(melt(clamp_num1), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 1") +
    theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num1). In the next version, this warning will become an error.No id variables; using all as measure variables

clamp_num2 <- clamp_model[, c("ChargeCycles", "SizeOfInitializedData", "SizeOfUninitializedData")]
ggplot(melt(clamp_num2), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1000000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 2") +
    theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num2). In the next version, this warning will become an error.No id variables; using all as measure variables

clamp_num3 <- clamp_model[, c("AddressOfEntryPoint", "BaseOfCode", "BaseOfData")]
ggplot(melt(clamp_num3), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1000000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 3") +
    theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num3). In the next version, this warning will become an error.No id variables; using all as measure variables

clamp_num4 <- clamp_model[, c("SizeOfCode", "CarMileage", "SizeOfHeaders")]
ggplot(melt(clamp_num4), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1000000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 4") +
    theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num4). In the next version, this warning will become an error.No id variables; using all as measure variables

clamp_num5 <- clamp_model[, c("CheckSum", "SizeOfStackReserve", "SizeOfStackCommit")]
ggplot(melt(clamp_num5), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1000000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 5") +
    theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num5). In the next version, this warning will become an error.No id variables; using all as measure variables

clamp_num6 <- clamp_model[, c("SizeOfHeapReserve", "SizeOfHeapCommit", "NumberOfSymbols")]
ggplot(melt(clamp_num6), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 10000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 6") +
    theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num6). In the next version, this warning will become an error.No id variables; using all as measure variables

Numerical variables: Univariate density analysis

ggplot(melt(clamp_num1), aes(x = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 1") +
  theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num1). In the next version, this warning will become an error.No id variables; using all as measure variables

ggplot(melt(clamp_num2), aes(x = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 2") +
  theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num2). In the next version, this warning will become an error.No id variables; using all as measure variables

ggplot(melt(clamp_num3), aes(x = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 3") +
  theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num3). In the next version, this warning will become an error.No id variables; using all as measure variables

ggplot(melt(clamp_num4), aes(x = value)) + 
  facet_wrap(~  variable, scales = 'free', ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 4") +
  theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num4). In the next version, this warning will become an error.No id variables; using all as measure variables

ggplot(melt(clamp_num5), aes(x = value)) + 
  facet_wrap(~  variable, scales = 'free', ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 5") +
  theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num5). In the next version, this warning will become an error.No id variables; using all as measure variables

ggplot(melt(clamp_num6), aes(x = value)) + 
  facet_wrap(~  variable, scales = 'free', ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 6") +
  theme(plot.title = element_text(hjust = 0.4))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(clamp_num6). In the next version, this warning will become an error.No id variables; using all as measure variables

Numerical analysis: Correlation plot

corrData <- copy(clamp_model)
corrData$MalwareDetection <- as.numeric(factor(corrData$MalwareDetection, levels = c("0", "1"), exclude = NULL))
# Correlation Matrix
corrDataNum = corrData[, lapply(corrData, is.numeric) == TRUE ]
corrplot(cor(corrDataNum), type = "upper", title = "Correlation Plot for Numeric Data", mar=c(0,0,1,0),
         tl.cex=0.5,
         tl.col = "black")

Categorical variables: Univariate barplot analysis

ClampCat1= clamp_model[, c("e_cblp", "e_cp", "e_cparhdr","e_minalloc", "e_maxalloc")]
ggplot(melt(ClampCat1, id.vars="e_maxalloc"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 1") +
  theme(plot.title = element_text(hjust = 0.5))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(ClampCat1). In the next version, this warning will become an error.attributes are not identical across measure variables; they will be dropped

ClampCat2= clamp_model[, c("e_maxalloc", "e_ss", "e_sp","e_csum", "e_ip")]
ggplot(melt(ClampCat2, id.vars="e_ip"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 2") +
  theme(plot.title = element_text(hjust = 0.5))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(ClampCat2). In the next version, this warning will become an error.attributes are not identical across measure variables; they will be dropped

ClampCat3= clamp_model[, c("e_ip", "e_cs", "e_lfarlc","e_ovno", "e_oemid")]
ggplot(melt(ClampCat3, id.vars="e_oemid"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 3") +
  theme(plot.title = element_text(hjust = 0.5))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(ClampCat3). In the next version, this warning will become an error.attributes are not identical across measure variables; they will be dropped

ClampCat4= clamp_model[, c("e_oemid", "e_oeminfo", "Models", "Magic", "PointerToSymbolTable")]
ggplot(melt(ClampCat4, id.vars="PointerToSymbolTable"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 4") +
  theme(plot.title = element_text(hjust = 0.5))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(ClampCat4). In the next version, this warning will become an error.attributes are not identical across measure variables; they will be dropped

ClampCat5= clamp_model[, c("PointerToSymbolTable", "SectionAlignment", "FileAlignment","MajorOperatingSystemVersion", "MinorOperatingSystemVersion")]
ggplot(melt(ClampCat5, id.vars="MinorOperatingSystemVersion"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 5") +
  theme(plot.title = element_text(hjust = 0.5))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(ClampCat5). In the next version, this warning will become an error.attributes are not identical across measure variables; they will be dropped

ClampCat6= clamp_model[, c("MinorOperatingSystemVersion", "SoftwareVersion", "OSVersion","Subsystem", "LoaderFlags")]
ggplot(melt(ClampCat6, id.vars="LoaderFlags"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 6") +
  theme(plot.title = element_text(hjust = 0.5))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(ClampCat6). In the next version, this warning will become an error.attributes are not identical across measure variables; they will be dropped

ClampCat7= clamp_model[, c("LoaderFlags", "MalwareDetection", "Country")]
ggplot(melt(ClampCat7, id.vars="Country"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 7") +
  theme(plot.title = element_text(hjust = 0.5))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(ClampCat7). In the next version, this warning will become an error.attributes are not identical across measure variables; they will be dropped

ggplot(clamp_model, aes(x = YearObtained)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of Year of Origination Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))


ggplot(clamp_model, aes(x = Characteristics)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of Characteristics Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))


ClampCat8= clamp_model[, c("MajorLinkerVersion", "MinorLinkerVersion", "MajorImageVersion")]
ggplot(melt(ClampCat8, id.vars="MajorImageVersion"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 8") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))
The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(ClampCat8). In the next version, this warning will become an error.attributes are not identical across measure variables; they will be dropped

ggplot(clamp_model, aes(x = MajorImageVersion)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of MajorImageVersion Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))


ggplot(clamp_model, aes(x = MinorImageVersion)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of MinorImageVersion Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))


ggplot(clamp_model, aes(x = DllCharacteristics)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of DllCharacteristics Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))


ggplot(clamp_model, aes(x = Country)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of Country Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))

Bivariate analysis

# Malware Detection against Country (Proportion)
plotdata <- clamp_model %>%
  group_by(Country, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))
`summarise()` regrouping output by 'Country' (override with `.groups` argument)
ggplot(plotdata, aes(fill=factor(plotdata$MalwareDetection), y=n, x=Country, group = Country)) + 
   geom_bar(width = 0.5, position="fill", stat="identity")+
   theme(axis.text.x = element_text(angle = 90, hjust=1), plot.title = element_text(hjust = 0.5))+
  labs(x = "Country", y = "Proportion", title = "MalwareDetection by Country", fill="Malware Detected")


# Malware Detection against Year (Proportion)
plotdata1 <- clamp_model %>%
  group_by(YearObtained, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))
`summarise()` regrouping output by 'YearObtained' (override with `.groups` argument)
ggplot(plotdata1, aes(fill=factor(plotdata1$MalwareDetection), y=n, x=YearObtained, group = YearObtained)) + 
   geom_bar(width = 0.5, position="fill", stat="identity") +
   theme(axis.text.x = element_text(angle = 90, hjust=1), plot.title = element_text(hjust = 0.5)) +
   labs(x = "Year", y = "Proportion", title = "Malware Detection by Year", fill="Malware Detected")


# Malware Detection by Software Version
plotdata2 <- clamp_model %>%
  group_by(SoftwareVersion, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))
`summarise()` regrouping output by 'SoftwareVersion' (override with `.groups` argument)
ggplot(plotdata2, aes(fill=factor(plotdata2$MalwareDetection), y=n, x=SoftwareVersion, group = SoftwareVersion)) + 
   geom_bar(width = 0.5, position="fill", stat="identity") +
   theme(axis.text.x = element_text(angle = 90, hjust=1), plot.title = element_text(hjust = 0.5)) +
   labs(x = "Software Version", y = "Proportion", title = "Malware Detection by Software Version", fill="Malware Detected")


plotdata3 <- clamp_model %>%
  group_by(OSVersion, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))
`summarise()` regrouping output by 'OSVersion' (override with `.groups` argument)
ggplot(plotdata3, aes(fill=factor(plotdata3$MalwareDetection), y=n, x=OSVersion, group = OSVersion)) + 
   geom_bar(width = 0.5, position="fill", stat="identity") +
   theme(axis.text.x = element_text(angle = 90, hjust=1), plot.title = element_text(hjust = 0.5)) +
   labs(x = "OS Version", y = "Proportion", title = "Malware Detection by OS Version", fill="Malware Detected")


plotdata4 <- clamp_model %>%
  group_by(Models, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))
`summarise()` regrouping output by 'Models' (override with `.groups` argument)
ggplot(plotdata4, aes(fill=factor(plotdata4$MalwareDetection), y=n, x= Models, group = Models)) + 
   geom_bar(width = 0.5, position="fill", stat="identity") +
   theme(plot.title = element_text(hjust = 0.5)) +
   labs(x = "Tesla Models", y = "Proportion", title = "Malware Detection by Model", fill="Malware Detected")

SMOTE

clamp_smoted <- clamp_model
table(clamp_smoted$MalwareDetection)

   0    1 
2501 2683 
proportion <- data.frame(table(clamp_smoted$MalwareDetection))
if (proportion$Freq[1]<proportion$Freq[2] | proportion$Freq[2]<proportion$Freq[1]){
  clamp_smoted <- SMOTE(MalwareDetection ~., clamp_smoted, perc.over = 100, k = 5, perc.under = 200)
}
(table(clamp_smoted$MalwareDetection))

   0    1 
5002 5002 

Association rules: Initial preparation

# Functions used in transforming continuous to discrete data
getBreaks <- function(column_name){
  min_value = 0
  max_value = max(column_name)
  interval = (max_value-min_value)/10
  #print(interval)
  breaks = c(seq(min_value, max_value, by=interval))
  breaks <- ceiling(breaks)
  return(breaks) 
}

getLabels <- function(column_name){
  breaks = getBreaks(column_name)
  #print(breaks)
  labels <- c()
  length <- length(breaks)
  #print(length)
  for (x in 0:length){
    #print(x)
    start <- breaks[x]
    oneStep <- x+1
    end <- breaks[oneStep]-1
    #print(start)
    #print(end)
    if (x == length){
      end <- start
      start <- breaks[x-1]
      string <- paste(toString(start), toString(end), sep="-") 
    } else{
      string <- paste(toString(start), toString(end), sep="-") 
    }
    #print(string)
    labels[x] <- string
  }
  #print(labels)
  deleted <- length - 1
  labels <- labels[-deleted]
  return(labels)
} 

#Splitting the continuous columns into intervals to make them discrete by step
clamp_trans <- clamp_model

clamp_nums <- unlist(lapply(clamp_trans, is.numeric))
clamp_nums <- clamp_trans[ , clamp_nums]
names(clamp_nums)
 [1] "NumberOfSections"        "NumberOfSymbols"         "SizeOfOptionalHeader"   
 [4] "SizeOfCode"              "SizeOfInitializedData"   "SizeOfUninitializedData"
 [7] "AddressOfEntryPoint"     "BaseOfCode"              "BaseOfData"             
[10] "ChargeCycles"            "CarMileage"              "SizeOfHeaders"          
[13] "CheckSum"                "SizeOfStackReserve"      "SizeOfStackCommit"      
[16] "SizeOfHeapReserve"       "SizeOfHeapCommit"        "NumberOfRvaAndSizes"    
clamp_nums$NumberOfSymbols <- cut(clamp_nums$NumberOfSymbols,
                            breaks = getBreaks(clamp_nums$NumberOfSymbols),
                            labels = getLabels(clamp_nums$NumberOfSymbols),
                            right = FALSE)
clamp_nums$SizeOfStackReserve <- cut(clamp_nums$SizeOfStackReserve,
                            breaks = getBreaks(clamp_nums$SizeOfStackReserve),
                            labels = getLabels(clamp_nums$SizeOfStackReserve),
                            right = FALSE)
clamp_nums$SizeOfInitializedData <- cut(clamp_nums$SizeOfInitializedData,
                            breaks = getBreaks(clamp_nums$SizeOfInitializedData),
                            labels = getLabels(clamp_nums$SizeOfInitializedData),
                            right = FALSE)
clamp_nums$SizeOfStackCommit <- cut(clamp_nums$SizeOfStackCommit,
                            breaks = getBreaks(clamp_nums$SizeOfStackCommit),
                            labels = getLabels(clamp_nums$SizeOfStackCommit),
                            right = FALSE)
clamp_nums$AddressOfEntryPoint <- cut(clamp_nums$AddressOfEntryPoint,
                            breaks = getBreaks(clamp_nums$AddressOfEntryPoint),
                            labels = getLabels(clamp_nums$AddressOfEntryPoint),
                            right = FALSE)
clamp_nums$BaseOfCode <- cut(clamp_nums$BaseOfCode,
                            breaks = getBreaks(clamp_nums$BaseOfCode),
                            labels = getLabels(clamp_nums$BaseOfCode),
                            right = FALSE)
clamp_nums$BaseOfData <- cut(clamp_nums$BaseOfData,
                            breaks = getBreaks(clamp_nums$BaseOfData),
                            labels = getLabels(clamp_nums$BaseOfData),
                            right = FALSE)
clamp_nums$ChargeCycles <- cut(clamp_nums$ChargeCycles,
                            breaks = getBreaks(clamp_nums$ChargeCycles),
                            labels = getLabels(clamp_nums$ChargeCycles),
                            right = FALSE)
clamp_nums$SizeOfHeapReserve <- cut(clamp_nums$SizeOfHeapReserve,
                            breaks = getBreaks(clamp_nums$SizeOfHeapReserve),
                            labels = getLabels(clamp_nums$SizeOfHeapReserve),
                            right = FALSE)
clamp_nums$SizeOfHeapCommit <- cut(clamp_nums$SizeOfHeapCommit,
                            breaks = getBreaks(clamp_nums$SizeOfHeapCommit),
                            labels = getLabels(clamp_nums$SizeOfHeapCommit),
                            right = FALSE)
clamp_nums$CarMileage <- cut(clamp_nums$CarMileage,
                            breaks = getBreaks(clamp_nums$CarMileage),
                            labels = getLabels(clamp_nums$CarMileage),
                            right = FALSE)

clamp_nums_name <- names(clamp_nums)
clamp_trans[, clamp_nums_name] <- list(NULL)
clamp_trans <- data.frame(clamp_trans, clamp_nums)

# Converting to transactional data
for(i in 1:ncol(clamp_trans)) clamp_trans[[i]] <- as.factor(clamp_trans[[i]])
trans <- as(clamp_trans, "transactions")

Association rules

rules <- apriori(data=trans, parameter=list(supp=0.45,conf = 0.85), appearance = list (default = "lhs", rhs="MalwareDetection=1"))
Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 2332 

set item appearances ...[1 item(s)] done [0.00s].
set transactions ...[4936 item(s), 5184 transaction(s)] done [0.06s].
sorting and recoding items ... [46 item(s)] done [0.01s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6
Mining stopped (time limit reached). Only patterns up to a length of 6 returned!
 done [17.55s].
writing ... [525 rule(s)] done [0.12s].
creating S4 object  ... done [0.18s].
inspect(head(rules))
    lhs                           rhs                    support confidence  coverage     lift count
[1] {OSVersion=V5,                                                                                  
     Subsystem=2,                                                                                   
     ChargeCycles=0-213365555} => {MalwareDetection=1} 0.4565972  0.8760178 0.5212191 1.692611  2367
[2] {MinorImageVersion=0,                                                                           
     Subsystem=2,                                                                                   
     ChargeCycles=0-213365555} => {MalwareDetection=1} 0.4594907  0.8878122 0.5175540 1.715400  2382
[3] {e_sp=184,                                                                                      
     OSVersion=V5,                                                                                  
     Subsystem=2,                                                                                   
     ChargeCycles=0-213365555} => {MalwareDetection=1} 0.4502315  0.8744848 0.5148534 1.689649  2334
[4] {OSVersion=V5,                                                                                  
     Subsystem=2,                                                                                   
     ChargeCycles=0-213365555,                                                                      
     CarMileage=0-8125235}     => {MalwareDetection=1} 0.4546682  0.8798059 0.5167824 1.699931  2357
[5] {e_cparhdr=4,                                                                                   
     OSVersion=V5,                                                                                  
     Subsystem=2,                                                                                   
     ChargeCycles=0-213365555} => {MalwareDetection=1} 0.4535108  0.8752792 0.5181327 1.691184  2351
[6] {e_lfarlc=64,                                                                                   
     OSVersion=V5,                                                                                  
     Subsystem=2,                                                                                   
     ChargeCycles=0-213365555} => {MalwareDetection=1} 0.4544753  0.8755110 0.5190972 1.691632  2356
rules_dataframe <- as(rules, 'data.frame')
rules_no <- apriori(data=trans, parameter=list(supp=0.40,conf = 0.6), appearance = list (default = "lhs", rhs="MalwareDetection=0"))
Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 2073 

set item appearances ...[1 item(s)] done [0.00s].
set transactions ...[4936 item(s), 5184 transaction(s)] done [0.06s].
sorting and recoding items ... [46 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6
Mining stopped (time limit reached). Only patterns up to a length of 6 returned!
 done [20.09s].
writing ... [180 rule(s)] done [0.15s].
creating S4 object  ... done [0.20s].
inspect(head(rules_no))
    lhs                               rhs                    support confidence  coverage     lift count
[1] {e_cp=3,                                                                                            
     SizeOfUninitializedData=0,                                                                         
     SizeOfHeapReserve=0-3355443,                                                                       
     SizeOfHeapCommit=0-13107}     => {MalwareDetection=0} 0.4558256  0.6031138 0.7557870 1.250117  2363
[2] {e_cp=3,                                                                                            
     SizeOfUninitializedData=0,                                                                         
     SizeOfStackReserve=0-3355443,                                                                      
     SizeOfHeapReserve=0-3355443}  => {MalwareDetection=0} 0.4533179  0.6019467 0.7530864 1.247698  2350
[3] {e_cp=3,                                                                                            
     SizeOfUninitializedData=0,                                                                         
     SizeOfStackReserve=0-3355443,                                                                      
     SizeOfHeapCommit=0-13107}     => {MalwareDetection=0} 0.4533179  0.6030280 0.7517361 1.249939  2350
[4] {e_cblp=144,                                                                                        
     SizeOfUninitializedData=0,                                                                         
     SizeOfHeapReserve=0-3355443,                                                                       
     SizeOfHeapCommit=0-13107}     => {MalwareDetection=0} 0.4558256  0.6028061 0.7561728 1.249479  2363
[5] {e_cblp=144,                                                                                        
     SizeOfUninitializedData=0,                                                                         
     SizeOfStackReserve=0-3355443,                                                                      
     SizeOfHeapReserve=0-3355443}  => {MalwareDetection=0} 0.4533179  0.6016385 0.7534722 1.247059  2350
[6] {e_cblp=144,                                                                                        
     SizeOfUninitializedData=0,                                                                         
     SizeOfStackReserve=0-3355443,                                                                      
     SizeOfHeapCommit=0-13107}     => {MalwareDetection=0} 0.4533179  0.6027186 0.7521219 1.249298  2350
rules_dataframe_no <- as(rules_no, 'data.frame')

Arules visualisation

library(arulesViz)
plot(rules, method='two-key plot')

#plot(rules, method='two-key plot', engine='interactive')
plot(rules, method = "paracoord")

plot(rules_no, method='two-key plot')

Normalising data for Neural Networks

normalize <- function(x) {
  return ((x - min(x)) / (max(x) - min(x)))
}
nums <- unlist(lapply(clamp_model, is.numeric))
clamp_num_nn <- clamp_model[ , nums]
normalized <- clamp_num_nn
normalized <- as.data.frame(lapply(normalized, normalize))
#names(mmnums)

clamp_fac_nn <- clamp_model
clamp_fac_nn[, names(clamp_num_nn)] <- list(NULL)
maxmindf <- data.frame(clamp_fac_nn, normalized)
str(maxmindf)
'data.frame':   5184 obs. of  52 variables:
 $ e_cblp                     : Factor w/ 9 levels "0","10","46",..: 8 8 8 8 8 5 8 8 8 8 ...
 $ e_cp                       : Factor w/ 7 levels "0","1","2","3",..: 4 4 4 4 4 3 4 4 4 4 ...
 $ e_cparhdr                  : Factor w/ 3 levels "0","2","4": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_minalloc                 : Factor w/ 4 levels "0","15","16",..: 1 1 1 1 1 2 1 1 1 1 ...
 $ e_maxalloc                 : Factor w/ 3 levels "0","17744","65535": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_ss                       : Factor w/ 2 levels "0","65520": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_sp                       : Factor w/ 8 levels "0","40","64",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ e_csum                     : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_ip                       : Factor w/ 4 levels "0","256","1047",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_cs                       : Factor w/ 4 levels "0","18293","18919",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_lfarlc                   : Factor w/ 3 levels "0","64","65": 2 2 2 2 2 2 2 2 2 2 ...
 $ e_ovno                     : Factor w/ 2 levels "0","26": 1 1 1 1 1 2 1 1 1 1 ...
 $ e_oemid                    : Factor w/ 2 levels "0","267": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_oeminfo                  : Factor w/ 3 levels "0","6","8": 1 1 1 1 1 1 1 1 1 1 ...
 $ Models                     : Factor w/ 3 levels "Model X","Model Y",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ YearObtained               : Factor w/ 36 levels "1970","1971",..: 16 9 22 21 22 4 22 22 22 20 ...
 $ PointerToSymbolTable       : Factor w/ 9 levels "0","36384","109088",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Characteristics            : Factor w/ 42 levels "34","35","258",..: 27 30 27 27 3 40 30 27 3 11 ...
 $ Magic                      : Factor w/ 2 levels "0","267": 2 2 2 2 2 2 2 2 2 2 ...
 $ MajorLinkerVersion         : Factor w/ 23 levels "0","1","2","3",..: 9 6 10 10 11 3 7 9 11 10 ...
 $ MinorLinkerVersion         : Factor w/ 36 levels "0","1","2","3",..: 1 11 1 1 11 22 1 1 11 1 ...
 $ SectionAlignment           : Factor w/ 6 levels "0","128","256",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ FileAlignment              : Factor w/ 7 levels "0","128","256",..: 4 4 4 4 4 4 7 7 4 4 ...
 $ MajorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","4",..: 4 4 5 6 6 2 4 4 6 5 ...
 $ MinorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ MajorImageVersion          : Factor w/ 41 levels "0","1","2","3",..: 1 1 1 7 7 1 1 1 7 1 ...
 $ MinorImageVersion          : Factor w/ 53 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ SoftwareVersion            : Factor w/ 6 levels "0","1","3","4",..: 5 4 5 5 6 4 4 4 6 5 ...
 $ OSVersion                  : Factor w/ 5 levels "V5","V4","V3",..: 2 1 1 1 3 1 1 1 3 1 ...
 $ Subsystem                  : Factor w/ 6 levels "0","1","2","3",..: 4 3 3 3 3 3 3 3 4 4 ...
 $ DllCharacteristics         : Factor w/ 25 levels "0","1","2","3",..: 5 1 7 12 17 1 1 1 17 17 ...
 $ LoaderFlags                : Factor w/ 6 levels "0","4357151",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ MalwareDetection           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ Country                    : Factor w/ 39 levels "Australia","Austria",..: 5 3 32 3 25 5 32 5 39 25 ...
 $ NumberOfSections           : num  0.0909 0.0909 0.1212 0 0.1212 ...
 $ NumberOfSymbols            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SizeOfOptionalHeader       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SizeOfCode                 : num  3.06e-04 1.14e-06 7.70e-06 0.00 3.28e-06 ...
 $ SizeOfInitializedData      : num  2.78e-03 3.15e-05 2.52e-04 1.08e-03 4.48e-04 ...
 $ SizeOfUninitializedData    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AddressOfEntryPoint        : num  0.025231 0.000181 0.000681 0 0.000316 ...
 $ BaseOfCode                 : num  9.7e-05 9.7e-05 9.7e-05 9.7e-05 9.7e-05 ...
 $ BaseOfData                 : num  2.62e-02 1.93e-04 7.73e-04 9.66e-05 3.86e-04 ...
 $ ChargeCycles               : num  0.55533 0.12581 0.12581 0.12581 0.00197 ...
 $ CarMileage                 : num  0.016434 0.000252 0.000756 0.001159 0.000706 ...
 $ SizeOfHeaders              : num  0.000442 0.000442 0.000442 0.000221 0.000442 ...
 $ CheckSum                   : num  2.78e-04 0.00 1.58e-05 2.65e-05 1.61e-05 ...
 $ SizeOfStackReserve         : num  0.03125 0.03125 0.03125 0.03125 0.00781 ...
 $ SizeOfStackCommit          : num  0.00195 0.00195 0.00195 0.00195 0.00391 ...
 $ SizeOfHeapReserve          : num  0.0312 0.0312 0.0312 0.0312 0.0312 ...
 $ SizeOfHeapCommit           : num  0.0312 0.0312 0.0312 0.0312 0.0312 ...
 $ NumberOfRvaAndSizes        : num  1 1 1 1 1 1 1 1 1 1 ...
mmtrain <- sample.split(Y = maxmindf$MalwareDetection, SplitRatio = 0.7)
mmtrainset <- subset(maxmindf, mmtrain == T)
mmtestset <- subset(maxmindf, mmtrain == F)

Training neural network model

nn_model <- nnet(MalwareDetection ~ ., data=mmtrainset, size=22, maxit=50, decay=1.0e-5, MaxNWts=15000)
# weights:  8999
initial  value 4195.517673 
iter  10 value 565.283776
iter  20 value 262.743727
iter  30 value 114.592404
iter  40 value 70.022117
iter  50 value 60.301821
final  value 60.301821 
stopped after 50 iterations
nn_predicted <- predict(nn_model, newdata=mmtestset, type="class")
confusionMatrix(as.factor(nn_predicted), mmtestset$MalwareDetection)
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 708  15
         1  42 790
                                          
               Accuracy : 0.9633          
                 95% CI : (0.9528, 0.9721)
    No Information Rate : 0.5177          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.9265          
                                          
 Mcnemar's Test P-Value : 0.0005736       
                                          
            Sensitivity : 0.9440          
            Specificity : 0.9814          
         Pos Pred Value : 0.9793          
         Neg Pred Value : 0.9495          
             Prevalence : 0.4823          
         Detection Rate : 0.4553          
   Detection Prevalence : 0.4650          
      Balanced Accuracy : 0.9627          
                                          
       'Positive' Class : 0               
                                          

Train test split

train <- sample.split(Y = clamp_model$MalwareDetection, SplitRatio = 0.7)
trainset <- subset(clamp_model, train == T)
testset <- subset(clamp_model, train == F)

Grid search algorithm and K-fold Cross Validation

grid_default <- expand.grid(n.trees = 200,
                           interaction.depth = 1,
                           shrinkage = 0.1,
                           n.minobsinnode = 10)

folds=10
cvIndex <- createFolds(factor(trainset$MalwareDetection), folds, returnTrain = T) #stratified k fold
train_control_log <- trainControl(
  index = cvIndex,
  number = folds,
  method = "cv",
)

Logistic Regression

logistic <- train(MalwareDetection~., data=trainset, trControl = train_control_log, method = "glm", family=binomial)
glm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurred
logreg_probs <- predict(logistic, newdata = testset, type = 'prob')
prediction from a rank-deficient fit may be misleading
threshold <- 0.5
logreg_predicted <- data.table(ifelse(logreg_probs > 0.5, 1, 0))
confusionMatrix(as.factor(logreg_predicted$`1`), testset$MalwareDetection)
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 720  55
         1  30 750
                                          
               Accuracy : 0.9453          
                 95% CI : (0.9329, 0.9561)
    No Information Rate : 0.5177          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.8907          
                                          
 Mcnemar's Test P-Value : 0.009237        
                                          
            Sensitivity : 0.9600          
            Specificity : 0.9317          
         Pos Pred Value : 0.9290          
         Neg Pred Value : 0.9615          
             Prevalence : 0.4823          
         Detection Rate : 0.4630          
   Detection Prevalence : 0.4984          
      Balanced Accuracy : 0.9458          
                                          
       'Positive' Class : 0               
                                          

Checking for multicollinearity

# logistic_check <- glm(MalwareDetection ~., data = trainset, family = binomial)
# car::vif(logistic_check)
# It returns an error: there are aliased coefficients in the model
# This means that we have ran into perfect multicollinearity.
# The column involved is "NumberOfRvaAndSizes" which is removed in feature selection process.

Random Forest

randomForest_model <- randomForest(
  MalwareDetection ~ .,
  data=trainset,
  tuneGrid = grid_default,
  trControl = train_control
)

rf_predicted <- predict(randomForest_model, newdata = testset)
confusionMatrix(rf_predicted, testset$MalwareDetection)
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 729   5
         1  21 800
                                         
               Accuracy : 0.9833         
                 95% CI : (0.9756, 0.989)
    No Information Rate : 0.5177         
    P-Value [Acc > NIR] : < 2.2e-16      
                                         
                  Kappa : 0.9665         
                                         
 Mcnemar's Test P-Value : 0.003264       
                                         
            Sensitivity : 0.9720         
            Specificity : 0.9938         
         Pos Pred Value : 0.9932         
         Neg Pred Value : 0.9744         
             Prevalence : 0.4823         
         Detection Rate : 0.4688         
   Detection Prevalence : 0.4720         
      Balanced Accuracy : 0.9829         
                                         
       'Positive' Class : 0              
                                         

Applying Feature Selection using Boruta

boruta <- Boruta(MalwareDetection ~ ., data = clamp_model, doTrace = 2, maxRuns=11)
 1. run of importance source...
 2. run of importance source...
 3. run of importance source...
 4. run of importance source...
 5. run of importance source...
 6. run of importance source...
 7. run of importance source...
 8. run of importance source...
 9. run of importance source...
 10. run of importance source...
#print(boruta)
plot(boruta, las = 2, cex.axis = 0.7)

#plotImpHistory(boruta)

bor <- TentativeRoughFix(boruta)
#print(bor)
attStats(bor)
#getSelectedAttributes(bor, withTentative = F)
selected_features <- getSelectedAttributes(bor, withTentative = F) 

clamp_selected <- clamp_model[, selected_features]
clamp_selected$MalwareDetection <- clamp_model$MalwareDetection

Normalising numerical data

normalize <- function(x) {
  return ((x - min(x)) / (max(x) - min(x)))
}
nums <- unlist(lapply(clamp_selected, is.numeric))
clamp_num_nn_s <- clamp_selected[ , nums]
normalized <- clamp_num_nn_s
normalized <- as.data.frame(lapply(normalized, normalize))
#names(mmnums)

clamp_fac_nn_s <- clamp_selected
clamp_fac_nn_s[, names(clamp_num_nn_s)] <- list(NULL)
maxmindf_s <- data.frame(clamp_fac_nn_s, normalized)
str(maxmindf_s)
'data.frame':   5184 obs. of  50 variables:
 $ e_cblp                     : Factor w/ 9 levels "0","10","46",..: 8 8 8 8 8 5 8 8 8 8 ...
 $ e_cp                       : Factor w/ 7 levels "0","1","2","3",..: 4 4 4 4 4 3 4 4 4 4 ...
 $ e_cparhdr                  : Factor w/ 3 levels "0","2","4": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_minalloc                 : Factor w/ 4 levels "0","15","16",..: 1 1 1 1 1 2 1 1 1 1 ...
 $ e_maxalloc                 : Factor w/ 3 levels "0","17744","65535": 3 3 3 3 3 3 3 3 3 3 ...
 $ e_ss                       : Factor w/ 2 levels "0","65520": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_sp                       : Factor w/ 8 levels "0","40","64",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ e_csum                     : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_ip                       : Factor w/ 4 levels "0","256","1047",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_cs                       : Factor w/ 4 levels "0","18293","18919",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ e_lfarlc                   : Factor w/ 3 levels "0","64","65": 2 2 2 2 2 2 2 2 2 2 ...
 $ e_ovno                     : Factor w/ 2 levels "0","26": 1 1 1 1 1 2 1 1 1 1 ...
 $ e_oemid                    : Factor w/ 2 levels "0","267": 1 1 1 1 1 1 1 1 1 1 ...
 $ e_oeminfo                  : Factor w/ 3 levels "0","6","8": 1 1 1 1 1 1 1 1 1 1 ...
 $ Models                     : Factor w/ 3 levels "Model X","Model Y",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ YearObtained               : Factor w/ 36 levels "1970","1971",..: 16 9 22 21 22 4 22 22 22 20 ...
 $ PointerToSymbolTable       : Factor w/ 9 levels "0","36384","109088",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Characteristics            : Factor w/ 42 levels "34","35","258",..: 27 30 27 27 3 40 30 27 3 11 ...
 $ Magic                      : Factor w/ 2 levels "0","267": 2 2 2 2 2 2 2 2 2 2 ...
 $ MajorLinkerVersion         : Factor w/ 23 levels "0","1","2","3",..: 9 6 10 10 11 3 7 9 11 10 ...
 $ MinorLinkerVersion         : Factor w/ 36 levels "0","1","2","3",..: 1 11 1 1 11 22 1 1 11 1 ...
 $ SectionAlignment           : Factor w/ 6 levels "0","128","256",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ FileAlignment              : Factor w/ 7 levels "0","128","256",..: 4 4 4 4 4 4 7 7 4 4 ...
 $ MajorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","4",..: 4 4 5 6 6 2 4 4 6 5 ...
 $ MinorOperatingSystemVersion: Factor w/ 10 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ MajorImageVersion          : Factor w/ 41 levels "0","1","2","3",..: 1 1 1 7 7 1 1 1 7 1 ...
 $ MinorImageVersion          : Factor w/ 53 levels "0","1","2","3",..: 1 1 1 2 3 1 1 1 3 1 ...
 $ SoftwareVersion            : Factor w/ 6 levels "0","1","3","4",..: 5 4 5 5 6 4 4 4 6 5 ...
 $ OSVersion                  : Factor w/ 5 levels "V5","V4","V3",..: 2 1 1 1 3 1 1 1 3 1 ...
 $ Subsystem                  : Factor w/ 6 levels "0","1","2","3",..: 4 3 3 3 3 3 3 3 4 4 ...
 $ DllCharacteristics         : Factor w/ 25 levels "0","1","2","3",..: 5 1 7 12 17 1 1 1 17 17 ...
 $ LoaderFlags                : Factor w/ 6 levels "0","4357151",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Country                    : Factor w/ 39 levels "Australia","Austria",..: 5 3 32 3 25 5 32 5 39 25 ...
 $ MalwareDetection           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ NumberOfSections           : num  0.0909 0.0909 0.1212 0 0.1212 ...
 $ NumberOfSymbols            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SizeOfOptionalHeader       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SizeOfCode                 : num  3.06e-04 1.14e-06 7.70e-06 0.00 3.28e-06 ...
 $ SizeOfInitializedData      : num  2.78e-03 3.15e-05 2.52e-04 1.08e-03 4.48e-04 ...
 $ SizeOfUninitializedData    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AddressOfEntryPoint        : num  0.025231 0.000181 0.000681 0 0.000316 ...
 $ BaseOfCode                 : num  9.7e-05 9.7e-05 9.7e-05 9.7e-05 9.7e-05 ...
 $ BaseOfData                 : num  2.62e-02 1.93e-04 7.73e-04 9.66e-05 3.86e-04 ...
 $ ChargeCycles               : num  0.55533 0.12581 0.12581 0.12581 0.00197 ...
 $ CarMileage                 : num  0.016434 0.000252 0.000756 0.001159 0.000706 ...
 $ CheckSum                   : num  2.78e-04 0.00 1.58e-05 2.65e-05 1.61e-05 ...
 $ SizeOfStackReserve         : num  0.03125 0.03125 0.03125 0.03125 0.00781 ...
 $ SizeOfStackCommit          : num  0.00195 0.00195 0.00195 0.00195 0.00391 ...
 $ SizeOfHeapReserve          : num  0.0312 0.0312 0.0312 0.0312 0.0312 ...
 $ SizeOfHeapCommit           : num  0.0312 0.0312 0.0312 0.0312 0.0312 ...
#maxmindf <- maxmindf %>% group_by(HasDetections) %>% sample_frac(.7)
#maxmindf <- one_hot(as.data.table(maxmindf))


mmtrain_s <- sample.split(Y = maxmindf_s$MalwareDetection, SplitRatio = 0.7)
mmtrainset_s <- subset(maxmindf_s, mmtrain == T)
mmtestset_s <- subset(maxmindf_s, mmtrain == F)

Training neural network

start.time <- Sys.time()
nn_model_s <- nnet(MalwareDetection ~ ., data=mmtrainset_s, size=22, maxit=50, decay=1.0e-5, MaxNWts=15000)
# weights:  8955
initial  value 2295.547194 
iter  10 value 568.649266
iter  20 value 238.634120
iter  30 value 109.412072
iter  40 value 71.103822
iter  50 value 60.547338
final  value 60.547338 
stopped after 50 iterations
nn_predicted_s <- predict(nn_model_s, newdata=mmtestset_s, type="class")
end.time <- Sys.time()
time.taken <- end.time - start.time
confusionMatrix(as.factor(nn_predicted_s), mmtestset_s$MalwareDetection)
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 718  17
         1  32 788
                                          
               Accuracy : 0.9685          
                 95% CI : (0.9586, 0.9766)
    No Information Rate : 0.5177          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.9369          
                                          
 Mcnemar's Test P-Value : 0.0455          
                                          
            Sensitivity : 0.9573          
            Specificity : 0.9789          
         Pos Pred Value : 0.9769          
         Neg Pred Value : 0.9610          
             Prevalence : 0.4823          
         Detection Rate : 0.4617          
   Detection Prevalence : 0.4727          
      Balanced Accuracy : 0.9681          
                                          
       'Positive' Class : 0               
                                          

Neural Network Graph

hiddenNodes <- c(1:30)
accuracy <- c()

for (i in c(1:30)) {
  nn_model <- nnet(MalwareDetection ~ ., data=mmtrainset, size=i, maxit=50, decay=1.0e-5, MaxNWts=15000)
  nn_predicted <- predict(nn_model, newdata=mmtestset, type="class")
  cm <- confusionMatrix(as.factor(nn_predicted), mmtestset$MalwareDetection)
  overall <- cm$overall['Accuracy']
  accuracy[i] <- overall
}
# weights:  410
initial  value 2671.982872 
iter  10 value 2126.076353
iter  20 value 1407.884143
iter  30 value 1196.138169
iter  40 value 1119.350089
iter  50 value 993.896612
final  value 993.896612 
stopped after 50 iterations
# weights:  819
initial  value 2673.989067 
iter  10 value 922.851035
iter  20 value 382.267766
iter  30 value 247.099615
iter  40 value 195.009754
iter  50 value 137.782835
final  value 137.782835 
stopped after 50 iterations
# weights:  1228
initial  value 3021.335418 
iter  10 value 593.152167
iter  20 value 346.071644
iter  30 value 273.037841
iter  40 value 231.001431
iter  50 value 209.236724
final  value 209.236724 
stopped after 50 iterations
# weights:  1637
initial  value 2801.006363 
iter  10 value 574.665580
iter  20 value 417.066881
iter  30 value 294.523259
iter  40 value 191.754139
iter  50 value 136.358484
final  value 136.358484 
stopped after 50 iterations
# weights:  2046
initial  value 2545.263804 
iter  10 value 741.687474
iter  20 value 526.899604
iter  30 value 481.641657
iter  40 value 461.502286
iter  50 value 431.749635
final  value 431.749635 
stopped after 50 iterations
# weights:  2455
initial  value 2990.123625 
iter  10 value 593.089519
iter  20 value 332.988040
iter  30 value 203.478787
iter  40 value 135.171911
iter  50 value 109.187541
final  value 109.187541 
stopped after 50 iterations
# weights:  2864
initial  value 2592.242402 
iter  10 value 571.854797
iter  20 value 237.321420
iter  30 value 123.476115
iter  40 value 72.360441
iter  50 value 60.412669
final  value 60.412669 
stopped after 50 iterations
# weights:  3273
initial  value 2491.936931 
iter  10 value 524.553265
iter  20 value 209.770882
iter  30 value 103.028849
iter  40 value 77.189834
iter  50 value 65.384928
final  value 65.384928 
stopped after 50 iterations
# weights:  3682
initial  value 2698.676744 
iter  10 value 574.151509
iter  20 value 291.560191
iter  30 value 190.517970
iter  40 value 133.648717
iter  50 value 108.605919
final  value 108.605919 
stopped after 50 iterations
# weights:  4091
initial  value 3029.962598 
iter  10 value 478.633536
iter  20 value 225.451755
iter  30 value 116.176413
iter  40 value 73.932974
iter  50 value 59.604135
final  value 59.604135 
stopped after 50 iterations
# weights:  4500
initial  value 2500.565414 
iter  10 value 494.758127
iter  20 value 270.433034
iter  30 value 157.178160
iter  40 value 86.650943
iter  50 value 63.221677
final  value 63.221677 
stopped after 50 iterations
# weights:  4909
initial  value 2861.710939 
iter  10 value 797.744459
iter  20 value 274.210555
iter  30 value 153.317117
iter  40 value 103.429514
iter  50 value 84.882964
final  value 84.882964 
stopped after 50 iterations
# weights:  5318
initial  value 2378.178728 
iter  10 value 513.421647
iter  20 value 234.947893
iter  30 value 115.498690
iter  40 value 76.266920
iter  50 value 59.833226
final  value 59.833226 
stopped after 50 iterations
# weights:  5727
initial  value 2756.368652 
iter  10 value 558.748167
iter  20 value 261.857410
iter  30 value 120.287767
iter  40 value 74.088022
iter  50 value 60.589526
final  value 60.589526 
stopped after 50 iterations
# weights:  6136
initial  value 3780.981689 
iter  10 value 704.636538
iter  20 value 280.500632
iter  30 value 150.561985
iter  40 value 84.642487
iter  50 value 68.387274
final  value 68.387274 
stopped after 50 iterations
# weights:  6545
initial  value 2667.008545 
iter  10 value 513.026124
iter  20 value 244.168848
iter  30 value 112.740442
iter  40 value 72.711167
iter  50 value 60.120899
final  value 60.120899 
stopped after 50 iterations
# weights:  6954
initial  value 3050.592090 
iter  10 value 543.629601
iter  20 value 280.074033
iter  30 value 136.988539
iter  40 value 84.753929
iter  50 value 63.002270
final  value 63.002270 
stopped after 50 iterations
# weights:  7363
initial  value 3990.891308 
iter  10 value 503.448214
iter  20 value 277.621518
iter  30 value 117.199154
iter  40 value 75.447335
iter  50 value 58.992919
final  value 58.992919 
stopped after 50 iterations
# weights:  7772
initial  value 3112.566160 
iter  10 value 614.027262
iter  20 value 328.173624
iter  30 value 147.676139
iter  40 value 83.185941
iter  50 value 64.023913
final  value 64.023913 
stopped after 50 iterations
# weights:  8181
initial  value 2498.444171 
iter  10 value 418.763493
iter  20 value 215.217376
iter  30 value 102.464652
iter  40 value 67.253166
iter  50 value 58.403429
final  value 58.403429 
stopped after 50 iterations
# weights:  8590
initial  value 2892.817994 
iter  10 value 430.922555
iter  20 value 222.182939
iter  30 value 114.451184
iter  40 value 70.256498
iter  50 value 60.898330
final  value 60.898330 
stopped after 50 iterations
# weights:  8999
initial  value 3269.031378 
iter  10 value 667.842892
iter  20 value 260.200717
iter  30 value 124.559795
iter  40 value 75.685170
iter  50 value 59.975700
final  value 59.975700 
stopped after 50 iterations
# weights:  9408
initial  value 2834.287033 
iter  10 value 428.919046
iter  20 value 227.116371
iter  30 value 102.462261
iter  40 value 72.267606
iter  50 value 59.816149
final  value 59.816149 
stopped after 50 iterations
# weights:  9817
initial  value 2567.013898 
iter  10 value 490.325549
iter  20 value 231.201564
iter  30 value 113.707183
iter  40 value 70.695450
iter  50 value 59.513310
final  value 59.513310 
stopped after 50 iterations
# weights:  10226
initial  value 4020.994758 
iter  10 value 580.138791
iter  20 value 306.579662
iter  30 value 196.483623
iter  40 value 138.645752
iter  50 value 87.361282
final  value 87.361282 
stopped after 50 iterations
# weights:  10635
initial  value 2826.197027 
iter  10 value 439.062768
iter  20 value 191.969730
iter  30 value 97.324507
iter  40 value 65.193102
iter  50 value 58.017921
final  value 58.017921 
stopped after 50 iterations
# weights:  11044
initial  value 2965.033025 
iter  10 value 521.295521
iter  20 value 228.361348
iter  30 value 95.235391
iter  40 value 65.470190
iter  50 value 56.666725
final  value 56.666725 
stopped after 50 iterations
# weights:  11453
initial  value 2349.076175 
iter  10 value 475.786309
iter  20 value 195.729425
iter  30 value 105.273608
iter  40 value 70.041168
iter  50 value 60.105414
final  value 60.105414 
stopped after 50 iterations
# weights:  11862
initial  value 3606.736557 
iter  10 value 470.569751
iter  20 value 258.697064
iter  30 value 126.105322
iter  40 value 75.554895
iter  50 value 61.646409
final  value 61.646409 
stopped after 50 iterations
# weights:  12271
initial  value 2670.285039 
iter  10 value 507.348814
iter  20 value 210.723451
iter  30 value 112.198396
iter  40 value 68.670631
iter  50 value 58.686489
final  value 58.686489 
stopped after 50 iterations
accuracy
 [1] 0.9106109 0.9633441 0.9588424 0.9581994 0.9556270 0.9691318 0.9646302 0.9614148 0.9575563 0.9646302
[11] 0.9672026 0.9646302 0.9665595 0.9639871 0.9614148 0.9652733 0.9665595 0.9646302 0.9659164 0.9633441
[21] 0.9678457 0.9710611 0.9646302 0.9627010 0.9684887 0.9652733 0.9627010 0.9639871 0.9594855 0.9633441
plot(hiddenNodes, accuracy, ylab="Model Accuracy", xlab="Number of Hidden Nodes")
lines(hiddenNodes, accuracy)


data <- data.frame(hiddenNodes, accuracy)
names(data) <- c("Number of Hidden Nodes", "Model Accuracy")

f <- list(
  family = "Courier New, monospace",
  size = 18,
  color = "#7f7f7f"
)
x <- list(
  title = "Number of Hidden Nodes",
  titlefont = f
)
y <- list(
  title = "Model Accuracy",
  titlefont = f
)
fig <- plot_ly(data, x = ~hiddenNodes, y = ~accuracy, type = 'scatter', mode = 'lines')
fig <- fig %>% layout(xaxis = x, yaxis = y)
fig
`arrange_()` is deprecated as of dplyr 0.7.0.
Please use `arrange()` instead.
See vignette('programming') for more help
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

Train Test Split

train_s <- sample.split(Y = clamp_selected$MalwareDetection, SplitRatio = 0.7)
trainset_s <- subset(clamp_selected, train == T)
testset_s <- subset(clamp_selected, train == F)

Logistic Regression

start.time <- Sys.time()
logistic_selected <- train(MalwareDetection~., data=trainset_s, trControl = train_control_log, method = "glm", family=binomial)
glm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: fitted probabilities numerically 0 or 1 occurredprediction from a rank-deficient fit may be misleadingglm.fit: fitted probabilities numerically 0 or 1 occurred
logreg_probs_s <- predict(logistic_selected, newdata = testset_s, type = 'prob')
prediction from a rank-deficient fit may be misleading
threshold <- 0.5
logreg_predicted_s <- data.table(ifelse(logreg_probs_s > 0.5, 1, 0))
end.time <- Sys.time()
time.taken <- end.time - start.time
confusionMatrix(as.factor(logreg_predicted_s$`1`), testset_s$MalwareDetection)
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 701  38
         1  49 767
                                          
               Accuracy : 0.9441          
                 95% CI : (0.9314, 0.9549)
    No Information Rate : 0.5177          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.8879          
                                          
 Mcnemar's Test P-Value : 0.2837          
                                          
            Sensitivity : 0.9347          
            Specificity : 0.9528          
         Pos Pred Value : 0.9486          
         Neg Pred Value : 0.9400          
             Prevalence : 0.4823          
         Detection Rate : 0.4508          
   Detection Prevalence : 0.4752          
      Balanced Accuracy : 0.9437          
                                          
       'Positive' Class : 0               
                                          
start.time <- Sys.time()
randomForest_model_s <- randomForest(
  MalwareDetection ~ .,
  data=trainset_s,
  tuneGrid = grid_default,
  trControl = train_control
)

rf_predicted_s <- predict(randomForest_model_s, newdata = testset_s)
end.time <- Sys.time()
time.taken <- end.time - start.time
confusionMatrix(rf_predicted_s, testset_s$MalwareDetection)
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 728   3
         1  22 802
                                          
               Accuracy : 0.9839          
                 95% CI : (0.9764, 0.9896)
    No Information Rate : 0.5177          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.9678          
                                          
 Mcnemar's Test P-Value : 0.0003182       
                                          
            Sensitivity : 0.9707          
            Specificity : 0.9963          
         Pos Pred Value : 0.9959          
         Neg Pred Value : 0.9733          
             Prevalence : 0.4823          
         Detection Rate : 0.4682          
   Detection Prevalence : 0.4701          
      Balanced Accuracy : 0.9835          
                                          
       'Positive' Class : 0               
                                          
csvMatrix <- confusionMatrix(rf_predicted_s, testset_s$MalwareDetection)
tocsv <- data.frame(cbind(t(csvMatrix$overall)))
write.csv(tocsv,file="csvMatrix.csv")
---
title: "2407 R Script"
output: html_notebook
---

Importing relevant libraries
```{r}
library(data.table)
library(mltools)
library(DMwR)
library(plyr)
library(dplyr)
library(caTools)
library(caret)
library(e1071)
library(corrplot)
library("arules")
library(nnet)
library(randomForest)
library(Boruta)
#install.packages("scales")
library(plotly)
#install.packages('arulesViz')
set.seed(1000)
```

Importing dataset for initial preparation
```{r}
clamp <- fread("ClaMP_Raw-5184.csv")
clamp <- lapply(clamp,  as.numeric)
clamp <- data.frame(clamp)
str(clamp)
```

Remove empty columns
```{r}
clamp$e_res <- NULL
clamp$e_res2 <- NULL
clamp$e_magic <- NULL
clamp$e_crlc <- NULL

row.has.na <- apply(clamp, 1, function(x){any(is.na(x))})
row.with.na <- clamp[row.has.na,]

str(clamp)
```
Based on the structure observed, we add and rename columns to make dataset more relevant for Tesla
```{r}
# Rename columns
colnames(clamp)[which(names(clamp) == "ImageBase")] <- "ChargeCycles"
colnames(clamp)[which(names(clamp) == "SizeOfImage")] <- "CarMileage"
colnames(clamp)[which(names(clamp) == "CreationYear")] <- "YearObtained"
colnames(clamp)[which(names(clamp) == "MajorSubsystemVersion")] <- "SoftwareVersion"
colnames(clamp)[which(names(clamp) == "MinorSubsystemVersion")] <- "OSVersion"
colnames(clamp)[which(names(clamp) == "Machine")] <- "Models"
colnames(clamp)[which(names(clamp) == "class")] <- "MalwareDetection"

# Adding in new column
teslacountries <- fread("TeslaCountries.csv")
clamp <- clamp %>% left_join(teslacountries, by = c("e_lfanew" = "CountryID"))
clamp$e_lfanew <- NULL
#clamp$Country <- as.factor(clamp$Country)
```

Converting to numeric columns
```{r}
clamp_num_names <- c("NumberOfSections", "NumberOfSymbols", "SizeOfOptionalHeader", "ChargeCycles",  "SizeOfInitializedData", "SizeOfUninitializedData", "AddressOfEntryPoint", "BaseOfCode", "BaseOfData", "SizeOfCode", "CarMileage", "SizeOfHeaders", "CheckSum", "SizeOfStackReserve", "SizeOfStackCommit", "SizeOfHeapReserve", "SizeOfHeapCommit", "NumberOfRvaAndSizes")
clamp_num <- clamp[names(clamp) %in% clamp_num_names]
num_names <- names(clamp_num)
clamp_num <- lapply(clamp_num, as.numeric)
clamp_num <- data.frame(clamp_num)
str(clamp_num)
```

Converting the remaining to categorical columns
```{r}
clamp_cat <- clamp
clamp_cat[, num_names] <- list(NULL)
clamp_cat <- lapply(clamp_cat, factor)
clamp_cat <- data.frame(clamp_cat)
str(clamp_cat)
```

Rename factor values (How to hide the revalue results?)
```{r}
clamp_cat$Models <- revalue(clamp_cat$Models, c("332"="Model X", "448"="Model Y", '34404'= 'Model S'))
clamp_cat$OSVersion <- revalue(clamp_cat$OSVersion, c("0"="V5", "1"="V4", '2'= 'V3', '10' = 'V2', '20' = 'V1'))
```


Final dataset
```{r}
clamp_model <- data.frame(clamp_cat, clamp_num)
str(clamp_model)
fwrite(clamp_model, file="TableauClampData.csv")
names(clamp_model)
clamp_corr <- clamp_model
clamp_corr <- data.frame(lapply(clamp_corr, as.numeric))
corrplot(cor(clamp_corr), type = "upper", title = "Correlation Plot for Final Dataset", mar=c(0,0,1,0),
         tl.cex=0.5,
         tl.col = "black")
str(clamp_model)
```

Data visualisation
Numerical variables: Univariate histogram analysis
```{r}
clamp_num1 <- clamp_model[, c("NumberOfSections", "NumberOfRvaAndSizes", "SizeOfOptionalHeader")]
ggplot(melt(clamp_num1), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 1") +
    theme(plot.title = element_text(hjust = 0.4))

clamp_num2 <- clamp_model[, c("ChargeCycles", "SizeOfInitializedData", "SizeOfUninitializedData")]
ggplot(melt(clamp_num2), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1000000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 2") +
    theme(plot.title = element_text(hjust = 0.4))

clamp_num3 <- clamp_model[, c("AddressOfEntryPoint", "BaseOfCode", "BaseOfData")]
ggplot(melt(clamp_num3), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1000000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 3") +
    theme(plot.title = element_text(hjust = 0.4))


clamp_num4 <- clamp_model[, c("SizeOfCode", "CarMileage", "SizeOfHeaders")]
ggplot(melt(clamp_num4), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1000000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 4") +
    theme(plot.title = element_text(hjust = 0.4))

clamp_num5 <- clamp_model[, c("CheckSum", "SizeOfStackReserve", "SizeOfStackCommit")]
ggplot(melt(clamp_num5), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 1000000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 5") +
    theme(plot.title = element_text(hjust = 0.4))

clamp_num6 <- clamp_model[, c("SizeOfHeapReserve", "SizeOfHeapCommit", "NumberOfSymbols")]
ggplot(melt(clamp_num6), aes(x = value)) + 
    facet_wrap(~ variable, scales = "free") + 
    geom_histogram(binwidth = 10000, fill = "indianred3", colour="black")+
    theme_minimal()+
    labs(x = "Factors", y = "Distribution", title = "Histogram of X Factors Part 6") +
    theme(plot.title = element_text(hjust = 0.4))
```

Numerical variables: Univariate density analysis
```{r}
ggplot(melt(clamp_num1), aes(x = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 1") +
  theme(plot.title = element_text(hjust = 0.4))

ggplot(melt(clamp_num2), aes(x = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 2") +
  theme(plot.title = element_text(hjust = 0.4))

ggplot(melt(clamp_num3), aes(x = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 3") +
  theme(plot.title = element_text(hjust = 0.4))

ggplot(melt(clamp_num4), aes(x = value)) + 
  facet_wrap(~  variable, scales = 'free', ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 4") +
  theme(plot.title = element_text(hjust = 0.4))

ggplot(melt(clamp_num5), aes(x = value)) + 
  facet_wrap(~  variable, scales = 'free', ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 5") +
  theme(plot.title = element_text(hjust = 0.4))

ggplot(melt(clamp_num6), aes(x = value)) + 
  facet_wrap(~  variable, scales = 'free', ncol=1) +
  geom_density(fill = "indianred3")+
  theme_minimal()+
  labs(x = "Factors", y = "Density", title = "Density Plot of X Factors Part 6") +
  theme(plot.title = element_text(hjust = 0.4))
```

Numerical analysis: Correlation plot
```{r}
corrData <- copy(clamp_model)
corrData$MalwareDetection <- as.numeric(factor(corrData$MalwareDetection, levels = c("0", "1"), exclude = NULL))
# Correlation Matrix
corrDataNum = corrData[, lapply(corrData, is.numeric) == TRUE ]
corrplot(cor(corrDataNum), type = "upper", title = "Correlation Plot for Numeric Data", mar=c(0,0,1,0),
         tl.cex=0.5,
         tl.col = "black")
```

Categorical variables: Univariate barplot analysis
```{r}
ClampCat1= clamp_model[, c("e_cblp", "e_cp", "e_cparhdr","e_minalloc", "e_maxalloc")]
ggplot(melt(ClampCat1, id.vars="e_maxalloc"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 1") +
  theme(plot.title = element_text(hjust = 0.5))

ClampCat2= clamp_model[, c("e_maxalloc", "e_ss", "e_sp","e_csum", "e_ip")]
ggplot(melt(ClampCat2, id.vars="e_ip"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 2") +
  theme(plot.title = element_text(hjust = 0.5))

ClampCat3= clamp_model[, c("e_ip", "e_cs", "e_lfarlc","e_ovno", "e_oemid")]
ggplot(melt(ClampCat3, id.vars="e_oemid"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 3") +
  theme(plot.title = element_text(hjust = 0.5))

ClampCat4= clamp_model[, c("e_oemid", "e_oeminfo", "Models", "Magic", "PointerToSymbolTable")]
ggplot(melt(ClampCat4, id.vars="PointerToSymbolTable"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 4") +
  theme(plot.title = element_text(hjust = 0.5))

ClampCat5= clamp_model[, c("PointerToSymbolTable", "SectionAlignment", "FileAlignment","MajorOperatingSystemVersion", "MinorOperatingSystemVersion")]
ggplot(melt(ClampCat5, id.vars="MinorOperatingSystemVersion"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 5") +
  theme(plot.title = element_text(hjust = 0.5))

ClampCat6= clamp_model[, c("MinorOperatingSystemVersion", "SoftwareVersion", "OSVersion","Subsystem", "LoaderFlags")]
ggplot(melt(ClampCat6, id.vars="LoaderFlags"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 6") +
  theme(plot.title = element_text(hjust = 0.5))

ClampCat7= clamp_model[, c("LoaderFlags", "MalwareDetection", "Country")]
ggplot(melt(ClampCat7, id.vars="Country"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 7") +
  theme(plot.title = element_text(hjust = 0.5))

ggplot(clamp_model, aes(x = YearObtained)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of Year of Origination Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))

ggplot(clamp_model, aes(x = Characteristics)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of Characteristics Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))

ClampCat8= clamp_model[, c("MajorLinkerVersion", "MinorLinkerVersion", "MajorImageVersion")]
ggplot(melt(ClampCat8, id.vars="MajorImageVersion"), aes(y = value)) + 
  facet_wrap(~ variable, scales = "free", ncol=2) +
  geom_bar(fill = "indianred3", 
           color="black")+
  theme_minimal()+
  theme(text = element_text(size=10))+
  labs(x = "Factors", y = "Levels", title = "Barplot of Categorical X Factors Part 8") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))

ggplot(clamp_model, aes(x = MajorImageVersion)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of MajorImageVersion Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))

ggplot(clamp_model, aes(x = MinorImageVersion)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of MinorImageVersion Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))

ggplot(clamp_model, aes(x = DllCharacteristics)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of DllCharacteristics Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))

ggplot(clamp_model, aes(x = Country)) + 
  geom_bar(fill = "indianred3", colour="black", width = 0.5, position = position_dodge(width = 5)) +
  labs(x = "Outcome", 
       y = "Count", 
       title = "Barplot of Country Distribution") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))
```

Bivariate analysis
```{r} 
# Malware Detection against Country (Proportion)
plotdata <- clamp_model %>%
  group_by(Country, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))

ggplot(plotdata, aes(fill=factor(plotdata$MalwareDetection), y=n, x=Country, group = Country)) + 
   geom_bar(width = 0.5, position="fill", stat="identity")+
   theme(axis.text.x = element_text(angle = 90, hjust=1), plot.title = element_text(hjust = 0.5))+
  labs(x = "Country", y = "Proportion", title = "MalwareDetection by Country", fill="Malware Detected")

# Malware Detection against Year (Proportion)
plotdata1 <- clamp_model %>%
  group_by(YearObtained, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))

ggplot(plotdata1, aes(fill=factor(plotdata1$MalwareDetection), y=n, x=YearObtained, group = YearObtained)) + 
   geom_bar(width = 0.5, position="fill", stat="identity") +
   theme(axis.text.x = element_text(angle = 90, hjust=1), plot.title = element_text(hjust = 0.5)) +
   labs(x = "Year", y = "Proportion", title = "Malware Detection by Year", fill="Malware Detected")

# Malware Detection by Software Version
plotdata2 <- clamp_model %>%
  group_by(SoftwareVersion, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))

ggplot(plotdata2, aes(fill=factor(plotdata2$MalwareDetection), y=n, x=SoftwareVersion, group = SoftwareVersion)) + 
   geom_bar(width = 0.5, position="fill", stat="identity") +
   theme(axis.text.x = element_text(angle = 90, hjust=1), plot.title = element_text(hjust = 0.5)) +
   labs(x = "Software Version", y = "Proportion", title = "Malware Detection by Software Version", fill="Malware Detected")

plotdata3 <- clamp_model %>%
  group_by(OSVersion, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))

ggplot(plotdata3, aes(fill=factor(plotdata3$MalwareDetection), y=n, x=OSVersion, group = OSVersion)) + 
   geom_bar(width = 0.5, position="fill", stat="identity") +
   theme(axis.text.x = element_text(angle = 90, hjust=1), plot.title = element_text(hjust = 0.5)) +
   labs(x = "OS Version", y = "Proportion", title = "Malware Detection by OS Version", fill="Malware Detected")

plotdata4 <- clamp_model %>%
  group_by(Models, MalwareDetection) %>%
  dplyr::summarize(n = n()) %>% 
  mutate(pct = n/sum(n),
         lbl = scales::percent(pct))

ggplot(plotdata4, aes(fill=factor(plotdata4$MalwareDetection), y=n, x= Models, group = Models)) + 
   geom_bar(width = 0.5, position="fill", stat="identity") +
   theme(plot.title = element_text(hjust = 0.5)) +
   labs(x = "Tesla Models", y = "Proportion", title = "Malware Detection by Model", fill="Malware Detected")
```

SMOTE
```{r}
clamp_smoted <- clamp_model
table(clamp_smoted$MalwareDetection)
proportion <- data.frame(table(clamp_smoted$MalwareDetection))
if (proportion$Freq[1]<proportion$Freq[2] | proportion$Freq[2]<proportion$Freq[1]){
  clamp_smoted <- SMOTE(MalwareDetection ~., clamp_smoted, perc.over = 100, k = 5, perc.under = 200)
}
(table(clamp_smoted$MalwareDetection))
```

Association rules: Initial preparation
```{r}
# Functions used in transforming continuous to discrete data
getBreaks <- function(column_name){
  min_value = 0
  max_value = max(column_name)
  interval = (max_value-min_value)/10
  #print(interval)
  breaks = c(seq(min_value, max_value, by=interval))
  breaks <- ceiling(breaks)
  return(breaks) 
}

getLabels <- function(column_name){
  breaks = getBreaks(column_name)
  #print(breaks)
  labels <- c()
  length <- length(breaks)
  #print(length)
  for (x in 0:length){
    #print(x)
    start <- breaks[x]
    oneStep <- x+1
    end <- breaks[oneStep]-1
    #print(start)
    #print(end)
    if (x == length){
      end <- start
      start <- breaks[x-1]
      string <- paste(toString(start), toString(end), sep="-") 
    } else{
      string <- paste(toString(start), toString(end), sep="-") 
    }
    #print(string)
    labels[x] <- string
  }
  #print(labels)
  deleted <- length - 1
  labels <- labels[-deleted]
  return(labels)
} 

#Splitting the continuous columns into intervals to make them discrete by step
clamp_trans <- clamp_model

clamp_nums <- unlist(lapply(clamp_trans, is.numeric))
clamp_nums <- clamp_trans[ , clamp_nums]
names(clamp_nums)

clamp_nums$NumberOfSymbols <- cut(clamp_nums$NumberOfSymbols,
                            breaks = getBreaks(clamp_nums$NumberOfSymbols),
                            labels = getLabels(clamp_nums$NumberOfSymbols),
                            right = FALSE)
clamp_nums$SizeOfStackReserve <- cut(clamp_nums$SizeOfStackReserve,
                            breaks = getBreaks(clamp_nums$SizeOfStackReserve),
                            labels = getLabels(clamp_nums$SizeOfStackReserve),
                            right = FALSE)
clamp_nums$SizeOfInitializedData <- cut(clamp_nums$SizeOfInitializedData,
                            breaks = getBreaks(clamp_nums$SizeOfInitializedData),
                            labels = getLabels(clamp_nums$SizeOfInitializedData),
                            right = FALSE)
clamp_nums$SizeOfStackCommit <- cut(clamp_nums$SizeOfStackCommit,
                            breaks = getBreaks(clamp_nums$SizeOfStackCommit),
                            labels = getLabels(clamp_nums$SizeOfStackCommit),
                            right = FALSE)
clamp_nums$AddressOfEntryPoint <- cut(clamp_nums$AddressOfEntryPoint,
                            breaks = getBreaks(clamp_nums$AddressOfEntryPoint),
                            labels = getLabels(clamp_nums$AddressOfEntryPoint),
                            right = FALSE)
clamp_nums$BaseOfCode <- cut(clamp_nums$BaseOfCode,
                            breaks = getBreaks(clamp_nums$BaseOfCode),
                            labels = getLabels(clamp_nums$BaseOfCode),
                            right = FALSE)
clamp_nums$BaseOfData <- cut(clamp_nums$BaseOfData,
                            breaks = getBreaks(clamp_nums$BaseOfData),
                            labels = getLabels(clamp_nums$BaseOfData),
                            right = FALSE)
clamp_nums$ChargeCycles <- cut(clamp_nums$ChargeCycles,
                            breaks = getBreaks(clamp_nums$ChargeCycles),
                            labels = getLabels(clamp_nums$ChargeCycles),
                            right = FALSE)
clamp_nums$SizeOfHeapReserve <- cut(clamp_nums$SizeOfHeapReserve,
                            breaks = getBreaks(clamp_nums$SizeOfHeapReserve),
                            labels = getLabels(clamp_nums$SizeOfHeapReserve),
                            right = FALSE)
clamp_nums$SizeOfHeapCommit <- cut(clamp_nums$SizeOfHeapCommit,
                            breaks = getBreaks(clamp_nums$SizeOfHeapCommit),
                            labels = getLabels(clamp_nums$SizeOfHeapCommit),
                            right = FALSE)
clamp_nums$CarMileage <- cut(clamp_nums$CarMileage,
                            breaks = getBreaks(clamp_nums$CarMileage),
                            labels = getLabels(clamp_nums$CarMileage),
                            right = FALSE)

clamp_nums_name <- names(clamp_nums)
clamp_trans[, clamp_nums_name] <- list(NULL)
clamp_trans <- data.frame(clamp_trans, clamp_nums)

# Converting to transactional data
for(i in 1:ncol(clamp_trans)) clamp_trans[[i]] <- as.factor(clamp_trans[[i]])
trans <- as(clamp_trans, "transactions")
```

Association rules
```{r}
rules <- apriori(data=trans, parameter=list(supp=0.45,conf = 0.85), appearance = list (default = "lhs", rhs="MalwareDetection=1"))
inspect(head(rules))
rules_dataframe <- as(rules, 'data.frame')
rules_no <- apriori(data=trans, parameter=list(supp=0.40,conf = 0.6), appearance = list (default = "lhs", rhs="MalwareDetection=0"))
inspect(head(rules_no))
rules_dataframe_no <- as(rules_no, 'data.frame')
```

Arules visualisation
```{r}
library(arulesViz)
plot(rules, method='two-key plot')
#plot(rules, method='two-key plot', engine='interactive')
plot(rules, method = "paracoord")
plot(rules_no, method='two-key plot')
```

Normalising data for Neural Networks
```{r}
normalize <- function(x) {
  return ((x - min(x)) / (max(x) - min(x)))
}
nums <- unlist(lapply(clamp_model, is.numeric))
clamp_num_nn <- clamp_model[ , nums]
normalized <- clamp_num_nn
normalized <- as.data.frame(lapply(normalized, normalize))
#names(mmnums)

clamp_fac_nn <- clamp_model
clamp_fac_nn[, names(clamp_num_nn)] <- list(NULL)
maxmindf <- data.frame(clamp_fac_nn, normalized)
str(maxmindf)

mmtrain <- sample.split(Y = maxmindf$MalwareDetection, SplitRatio = 0.7)
mmtrainset <- subset(maxmindf, mmtrain == T)
mmtestset <- subset(maxmindf, mmtrain == F)
```

Training neural network model
```{r}
nn_model <- nnet(MalwareDetection ~ ., data=mmtrainset, size=22, maxit=50, decay=1.0e-5, MaxNWts=15000)

nn_predicted <- predict(nn_model, newdata=mmtestset, type="class")
confusionMatrix(as.factor(nn_predicted), mmtestset$MalwareDetection)
```

Train test split
```{r}
train <- sample.split(Y = clamp_model$MalwareDetection, SplitRatio = 0.7)
trainset <- subset(clamp_model, train == T)
testset <- subset(clamp_model, train == F)
```

Grid search algorithm and K-fold Cross Validation
```{r}
grid_default <- expand.grid(n.trees = 200,
                           interaction.depth = 1,
                           shrinkage = 0.1,
                           n.minobsinnode = 10)

folds=10
cvIndex <- createFolds(factor(trainset$MalwareDetection), folds, returnTrain = T) #stratified k fold
train_control_log <- trainControl(
  index = cvIndex,
  number = folds,
  method = "cv",
)
```

Logistic Regression
```{r}
logistic <- train(MalwareDetection~., data=trainset, trControl = train_control_log, method = "glm", family=binomial)

logreg_probs <- predict(logistic, newdata = testset, type = 'prob')
threshold <- 0.5
logreg_predicted <- data.table(ifelse(logreg_probs > 0.5, 1, 0))
confusionMatrix(as.factor(logreg_predicted$`1`), testset$MalwareDetection)
```
Checking for multicollinearity 
```{r}
# logistic_check <- glm(MalwareDetection ~., data = trainset, family = binomial)
# car::vif(logistic_check)
# It returns an error: there are aliased coefficients in the model
# This means that we have ran into perfect multicollinearity.
# The column involved is "NumberOfRvaAndSizes" which is removed in feature selection process.
```

Random Forest
```{r}
randomForest_model <- randomForest(
  MalwareDetection ~ .,
  data=trainset,
  tuneGrid = grid_default,
  trControl = train_control
)

rf_predicted <- predict(randomForest_model, newdata = testset)
confusionMatrix(rf_predicted, testset$MalwareDetection)
```

Applying Feature Selection using Boruta
```{r}
boruta <- Boruta(MalwareDetection ~ ., data = clamp_model, doTrace = 2, maxRuns=11)
#print(boruta)
plot(boruta, las = 2, cex.axis = 0.7)
#plotImpHistory(boruta)

bor <- TentativeRoughFix(boruta)
#print(bor)
attStats(bor)
#getSelectedAttributes(bor, withTentative = F)
selected_features <- getSelectedAttributes(bor, withTentative = F) 

clamp_selected <- clamp_model[, selected_features]
clamp_selected$MalwareDetection <- clamp_model$MalwareDetection
```

Normalising numerical data
```{r}
normalize <- function(x) {
  return ((x - min(x)) / (max(x) - min(x)))
}
nums <- unlist(lapply(clamp_selected, is.numeric))
clamp_num_nn_s <- clamp_selected[ , nums]
normalized <- clamp_num_nn_s
normalized <- as.data.frame(lapply(normalized, normalize))
#names(mmnums)

clamp_fac_nn_s <- clamp_selected
clamp_fac_nn_s[, names(clamp_num_nn_s)] <- list(NULL)
maxmindf_s <- data.frame(clamp_fac_nn_s, normalized)
str(maxmindf_s)

#maxmindf <- maxmindf %>% group_by(HasDetections) %>% sample_frac(.7)
#maxmindf <- one_hot(as.data.table(maxmindf))


mmtrain_s <- sample.split(Y = maxmindf_s$MalwareDetection, SplitRatio = 0.7)
mmtrainset_s <- subset(maxmindf_s, mmtrain == T)
mmtestset_s <- subset(maxmindf_s, mmtrain == F)
```

Training neural network
```{r}
start.time <- Sys.time()
nn_model_s <- nnet(MalwareDetection ~ ., data=mmtrainset_s, size=22, maxit=50, decay=1.0e-5, MaxNWts=15000)

nn_predicted_s <- predict(nn_model_s, newdata=mmtestset_s, type="class")
end.time <- Sys.time()
time.taken <- end.time - start.time
confusionMatrix(as.factor(nn_predicted_s), mmtestset_s$MalwareDetection)


```

Neural Network Graph
```{r}
hiddenNodes <- c(1:30)
accuracy <- c()

for (i in c(1:30)) {
  nn_model <- nnet(MalwareDetection ~ ., data=mmtrainset, size=i, maxit=50, decay=1.0e-5, MaxNWts=15000)
  nn_predicted <- predict(nn_model, newdata=mmtestset, type="class")
  cm <- confusionMatrix(as.factor(nn_predicted), mmtestset$MalwareDetection)
  overall <- cm$overall['Accuracy']
  accuracy[i] <- overall
}

accuracy

plot(hiddenNodes, accuracy, ylab="Model Accuracy", xlab="Number of Hidden Nodes")
lines(hiddenNodes, accuracy)

data <- data.frame(hiddenNodes, accuracy)
names(data) <- c("Number of Hidden Nodes", "Model Accuracy")

f <- list(
  family = "Courier New, monospace",
  size = 18,
  color = "#7f7f7f"
)
x <- list(
  title = "Number of Hidden Nodes",
  titlefont = f
)
y <- list(
  title = "Model Accuracy",
  titlefont = f
)
fig <- plot_ly(data, x = ~hiddenNodes, y = ~accuracy, type = 'scatter', mode = 'lines')
fig <- fig %>% layout(xaxis = x, yaxis = y)
fig
```

Train Test Split
```{r}
train_s <- sample.split(Y = clamp_selected$MalwareDetection, SplitRatio = 0.7)
trainset_s <- subset(clamp_selected, train == T)
testset_s <- subset(clamp_selected, train == F)
```

Logistic Regression
```{r}
start.time <- Sys.time()
logistic_selected <- train(MalwareDetection~., data=trainset_s, trControl = train_control_log, method = "glm", family=binomial)

logreg_probs_s <- predict(logistic_selected, newdata = testset_s, type = 'prob')
threshold <- 0.5
logreg_predicted_s <- data.table(ifelse(logreg_probs_s > 0.5, 1, 0))
end.time <- Sys.time()
time.taken <- end.time - start.time
confusionMatrix(as.factor(logreg_predicted_s$`1`), testset_s$MalwareDetection)

```

```{r}
start.time <- Sys.time()
randomForest_model_s <- randomForest(
  MalwareDetection ~ .,
  data=trainset_s,
  tuneGrid = grid_default,
  trControl = train_control
)

rf_predicted_s <- predict(randomForest_model_s, newdata = testset_s)
end.time <- Sys.time()
time.taken <- end.time - start.time
confusionMatrix(rf_predicted_s, testset_s$MalwareDetection)

csvMatrix <- confusionMatrix(rf_predicted_s, testset_s$MalwareDetection)
tocsv <- data.frame(cbind(t(csvMatrix$overall)))
write.csv(tocsv,file="csvMatrix.csv")
```

